Remote Evaluation of Content Delivery Service

ABSTRACT

Systems, methods, and apparatuses for remote evaluation of content delivery service are described. A remote computing device may determine the existence and performance characteristics of devices and equipment at a premises by monitoring and analyzing data from a network associated with the premises to determine the presence or absence of equipment, compatibility issues, and performance quality.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent claims the benefit of U.S. Provisional Application No.62/554,009, filed on Sep. 4, 2017 and entitled “Remote Evaluation ofContent Delivery Service,” and claims the benefit of U.S. ProvisionalApplication No. 62/677,166, filed on May 28, 2018 and entitled “RemoteEvaluation of Content Delivery Service.” U.S. Provisional ApplicationNo. 62/554,009 and U.S. Provisional Application No. 62/677,166 arehereby incorporated by reference herein in their entireties.

BACKGROUND

Content delivery services (e.g., networks) often comprise numerousdevices and/or different types of equipment. It can be difficult tomonitor such services for failures or performance. For example, it maybe difficult to determine the presence or absence of filters (such asPOE filters), amplifiers, splitters, the integrity of connections, thesettings/status/configurations of other network hardware and/or softwarecomponents, and/or other network issues.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some of the disclosure. The summary is not an extensiveoverview of the disclosure. It is neither intended to identify key orcritical elements of the disclosure nor to delineate the scope of thedisclosure. The following summary merely presents some concepts of thedisclosure in a simplified form as a prelude to the description below.

Remote localization of impairments and aggregation of performance datavia client deployed devices may reduce the amount of dedicated testinstrumentations. Determining the existence and performancecharacteristics of equipment at a premises by remotely monitoring andanalyzing data from a network associated with the premises may beadvantageous.

BRIEF DESCRIPTION OF THE DRAWINGS

Some features herein are shown by way of example, and not by way oflimitation, in the figures of the accompanying drawings and in whichlike reference numerals refer to similar elements.

FIG. 1 shows a communication network.

FIG. 2 shows hardware elements of a computing device.

FIG. 3 shows an environment comprising a diagnostic server incommunication with a home network.

FIG. 4A is a flow chart showing a method for generating a ComplimentaryCumulative Density Function.

FIGS. 4B-4D show example visual representations of data outputs from theflow chart in FIG. 4A.

FIGS. 5-6B show example graphical illustrations of example complimentarycumulative density functions.

FIG. 7 is a flow chart showing a method for determining incompatibledevices in a home network.

FIG. 8 shows an example graphical representation of suckout.

FIGS. 9A-9C show example graphical illustrations of continuous waveswith respect to path loss.

FIG. 10 is a flow chart showing a method for determining whether a pointof entry filter is installed in a home network.

FIG. 11A shows an example graphical illustration of reflection pathswithin a home network.

FIG. 11B shows an example graphical illustration of a result of the flowchart of FIG. 4A with respect to whether a home network comprises apoint of entry filter.

FIG. 12 is a flow chart showing a method for determining whether a pointof entry filter is installed in a home network.

FIG. 13 shows an example graphical representation of a multimedia overcoaxial alliance system with a point of entry filter and an echoresponse.

FIGS. 14A-14C and 15A-15B shows example illustrations of various versionof channel estimation graphs.

FIG. 16 is a flow chart showing a method for determining whether a pointof entry filter is installed in a home network.

FIG. 17 show an examples of Data Over Cable Service InterfaceSpecification devices and corresponding reflections.

FIG. 18 is a flow chart showing a method for determining whether a homenetwork contains service-impacting impairments.

FIGS. 19A-19F show example channel estimations with varying echo delaysand their corresponding inverse fast Fourier transform waveforms.

FIG. 20 is a flow chart showing a method for qualifying or disqualifyingmidsplit candidates.

FIG. 21 is a flow chart showing a method for remotely monitoring probedata in a home network.

FIG. 22 shows an example graphical representation of an OP2OP isolationof MoCA ExD OFDM Beacon Signals from channels D1 thru D10.

FIG. 23 shows an example graphical representation of a loop-throughtopology of a home network and a home-run topology of a home network.

FIGS. 24-29 show example graphical representations of ACA EVM probe dataas described herein.

FIG. 30 shows an example suckout in a PTP EVM probe response.

FIG. 31 shows a home network as described herein.

FIG. 32 shows a normalized MoCA RF channel response with and without acorrectly installed POE filter.

FIG. 33 shows an EVM probe overlay of two nodes.

FIG. 34 shows a delay signal with positive tilt.

FIG. 35 shows an ACA response tilt.

FIG. 36 shows an example of a MoCA channel OFDM tilt.

FIG. 37 shows an example 3-point moving average tilt.

FIG. 38 shows an example 10-point moving average tilt.

FIGS. 39-40 show an OFDM Left Sidebands tilt.

FIGS. 41-42 show an OFDM Right Sidebands.

FIG. 43 shows an example of an OFDM Left Sideband Tilt Crossing.

FIG. 44 shows an example of an OFDM Right Sideband Tilt Crossing.

FIG. 45-46 show examples of MoCA OFDM bit loads.

FIG. 47 shows an example of a line-crossing check algorithm.

FIG. 48A shows an example POE and two-way splitter connected to twonodes.

FIG. 48B shows an example POE Tilt Crossing.

FIG. 49A-49C show an example Fourier analysis of probe data.

FIG. 50A-50C show examples of skewness and suckout.

FIG. 51 shows an example EVM for a POE with a two-way splitter.

FIG. 52 shows an example POE and an eight-way splitter.

FIG. 53 shows example configurations of a two-way splitter, two nodes,and/or a POE.

FIG. 54A-54E show example POE impacts based on various cableconfigurations.

FIG. 55 shows an example transmitter node with an eight-way splitter.

FIG. 56 is a flow chart showing a method for determining whether a pointof entry filter is installed in a home network and its distance.

DETAILED DESCRIPTION

In the following description, reference is made to the accompanyingdrawings, which form a part hereof, and in which is shown by way ofillustration of various non-limiting ways the disclosure that may bepracticed.

As set forth in Table 1, the following abbreviations may correspond tothe following within this disclosure:

TABLE 1 ACA Alternative Channel Assessment bps bits-per-second bpsymbits-per-symbol BPSK Bi-Phase Shift Keying CNR Carrier-to-Noise Ratio CMCable Modem dB decibel DS Downstream DStat Descriptive Statistics DUTDevice Under Test EVM Error Vector Magnitude FEC Forward ErrorCorrection HFC Hybrid fiber-coax HP High Pass HTTP Hyper Text TransferProtocol Hz Hertz Kbps Kilobits per second KPI Key Performance IndicatorLCI LDPC Code Iteration LDPC low-density parity check LP Low Pass OFDMOrthogonal Frequency-Division Multiplexing OSS Operation SoftwareSupport PDF Probability Density Function PNM Proactive NetworkMaintenance POE Point-OF-Entry (MoCA Band-Stop Filter) MER ModulationError Ratio MoCA Media over Cable Alliance MR Micro Reflection NANetwork Analyzer NVP Nominal Velocity of Propagation RF Radio FrequencyRL Return Loss RT Round Trip SA Spectrum Analyzer SCTE Society of CableTelecommunications Engineers SD Standard Deviation (σ) SNRSignal-To-Noise Ratio SNMP Simple Network Management Protocol SWRStanding Wave Ratio TDR Time-Domain Reflectometer VSA Vector SignalAnalyzer VSWR Voltage Standing Wave Ratio WMA Window Moving Average WSWindow Size

With home networks comprising a number of devices and equipment andservice providers managing a number of home networks, remote monitoringand diagnosis may be extremely advantageous. Many factors, such as thepresence or absence of point of entry (POE) filters, incompatibledevices, and/or improper installation of devices and equipment, may beimportant to not only identify, but also trigger remedial measures.Furthermore, home networks, such as a MoCA network, may allow variousdevices within a premises to communicate with one another, and theconnections between any two of these devices may need to be of a certainminimum quality. Accordingly, it is advantageous to identify thepresence or absence of POE filters, incompatible devices, and/or theirlocations to properly assess network configurations. Additionally,monitoring the quality of such networks is equally advantageous. Remotemonitoring and identification may quickly determine otherwise overlookedimproper network configurations and quality issues.

In some access networks and home networks, various activities (e.g.,upstream and downstream data) may operate on mutually exclusivefrequency bands. Access networks may exist both in and outside a homeand may deliver voice, video, and high speed data from a centralizedlocation (e.g., a hub) and/or distributed locations to other nodes(e.g., homes and/or offices) via transport technologies, such asEthernet, Data Over Cable Service Interface Specification (DOCSIS),and/or other transport mechanisms. End point networks (e.g., home oroffice networks), may be located proximate to a home or office (e.g.,within a home) and provide connectivity between client premise equipment(CPE) and/or with other networks (e.g., wired and/or wireless networks)and may be configured to support services such as any room DVR, VoIP,Wi-Fi, wireless, and/or data, audio, and/or video delivery services.Examples of home network technologies include MoCA and Wi-Fi.

Diagnostic solutions may assist in providing root cause identificationof network components contributing to service issues. Mesh networks maybe configured to provide comprehensive multipoint-to-multipoint (MPTMP)home network analysis and/or characterization and can be configured tobe redundant and to provide substantial detail with respect to ProactiveNetwork Maintenance solutions.

In a DOCSIS network, some overlap may exist between upstream operatingbands (e.g., on the distribution network side, for example, upstream ofthe gateway) and home/office network operating bands (e.g., on thehome/office side, for example, downstream of the gateway). As providerscontinue to upgrade their networks, there may be a spectrum overlapbetween 1,125 and 1,218 MHz, which may represent approximately 17% ofthe MoCA band. The remaining 83% of the MoCA band may be invisible tonewer DOCSIS 3.1 devices.

The DOCSIS 3.0 operating band may be between 5-1,002 MHz. The DOCSIS 3.1operating band may be between 5-1,218 MHz (via Fiber Deep markets).

In some diagnostics (e.g., DOCSIS), there may be a potential that asubset of the communication paths that exist in a home may be obscuredfor MoCA devices. Mesh technologies such as multi-point to multi-pointtechnologies using, for example, MoCA and Wi-Fi, may be better adaptedto characterize and/or analyze a home network across some frequencies.Such configurations may have additional visibility into certain networkoperating bands of a home/office.

MoCA home network diagnostics may leverage full mesh rate (FMR) tables,which may represent throughput capabilities between some and/or alldevices associated with a network (e.g., a mesh network). A rate table(e.g., a partial mesh or full mesh rate table) is provided below (Table2).

TABLE 2 Node 1 2 3 1 — 201 Mbps 208 Mbps 2 149 Mbps — 270 Mbps 3 213Mbps 205 Mbps —

Rates may be measured between various devices in a network. Rates thatfail to meet some pre-defined threshold, for example 200 Mbps for MoCA1.1, may be flagged for further investigation. Additional diagnosticsmay be run on the connection to attempt to isolate and/or qualify theissue. In certain circumstances, repairs may be made remotely. In othercircumstances, a field technician may be called to make a repair or toconduct additional on-site inspection and/or diagnosis. Failure to meeta performance threshold (e.g., throughput) may be caused by a variety ofhome network defects and/or deficiencies such as damaged cables, looseconnectors, incompatible drop amplifiers and splitters, VoIP portconnections, and missing point-of entry filters, and other hardwareand/or software issues. FMR values alone do not diagnose, and isolatedefects.

Without a point of entry filter, a bridge may form between two homenetworks, forming a single combined network. Different home networks areintended to be two separate networks, isolated from one another. Pointof Entry filters are useful devices to prevent bridging in MoCA enabledhomes. Some MoCA enabled homes do not have these devices installedand/or installed correctly. The Point of Entry filters may beinadvertently removed by a client, not included during installation, notinstalled properly, and/or may be damaged in some way. Bridging eventsmay be detected after they occur, but network performance and/orsecurity may be compromised, and the client home network performance maydegrade. In addition to preventing bridging, Point of Entry filters mayenhance MoCA network performance by reflecting MoCA signals (echoes)back into the home.

Remote evaluation of the structure and performance characteristics ofhome networks connected to content delivery access networks may allow acontent provider to identify structural and performance issues thataffect individual clients, a sub-population of clients, and/or allclients. Additionally, such evaluation may enable a provider to correctidentified structural and/or performance issues to provide improvedcontent delivery service, such as by increasing content delivery speedand bandwidth and/or by decreasing interference and interruptions.

FIG. 1 shows a communication network 100 in which features describedherein may be implemented. The communication network 100 may compriseone or more information distribution networks of any type, such as,without limitation, a telephone network, a wireless network (e.g., anLTE network, a 5G network, a WiFi IEEE 802.11 network, a WiMAX network,a satellite network, and/or any other network for wirelesscommunication), an optical fiber network, a coaxial cable network,and/or a hybrid fiber/coax distribution network. The communicationnetwork 100 may use a series of interconnected communication links 101(e.g., coaxial cables, optical fibers, wireless links, etc.) to connectmultiple premises 102 (e.g., businesses, homes, consumer dwellings,train stations, airports, etc.) to a local office 103 (e.g., a headend).The local office 103 may send downstream information signals and receiveupstream information signals via the communication links 101. Each ofthe premises 102 may comprise devices, described below, to receive,send, and/or otherwise process those signals and information containedtherein.

The communication links 101 may originate from the local office 103 andmay comprise components not shown, such as splitters, filters,amplifiers, etc., to help convey signals clearly. The communicationlinks 101 may be coupled to one or more wireless access points 127configured to communicate with one or more mobile devices 125 via one ormore wireless networks. The mobile devices 125 may comprise smartphones, tablets or laptop computers with wireless transceivers, tabletsor laptop computers communicatively coupled to other devices withwireless transceivers, and/or any other type of device configured tocommunicate via a wireless network.

The local office 103 may comprise an interface 104, such as atermination system (TS). The interface 104 may comprise a cable modemtermination system (CMTS) and/or other computing device(s) configured tosend information downstream to, and to receive information upstreamfrom, devices communicating with the local office 103 via thecommunications links 101. The interface 104 may be configured to managecommunications among those devices, to manage communications betweenthose devices and backend devices such as servers 105-107 and 122,and/or to manage communications between those devices and one or moreexternal networks 109. The local office 103 may comprise one or morenetwork interfaces 108 that comprise circuitry needed to communicate viathe external networks 109. The external networks 109 may comprisenetworks of Internet devices, telephone networks, wireless networks,wireless networks, fiber optic networks, and/or any other desirednetwork. The local office 103 may also or alternatively communicate withthe mobile devices 125 via the interface 108 and one or more of theexternal networks 109, e.g., via one or more of the wireless accesspoints 127.

The push notification server 105 may be configured to generate pushnotifications to deliver information to devices in the premises 102and/or to the mobile devices 125. The content server 106 may beconfigured to provide content to devices in the premises 102 and/or tothe mobile devices 125. This content may comprise, for example, video,audio, text, web pages, images, files, etc. The content server 106 (or,alternatively, an authentication server) may comprise software tovalidate user identities and entitlements, to locate and retrieverequested content, and/or to initiate delivery (e.g., streaming) of thecontent. The application server 107 may be configured to offer anydesired service. For example, an application server may be responsiblefor collecting, and generating a download of, information for electronicprogram guide listings. Another application server may be responsiblefor monitoring user viewing habits and collecting information from thatmonitoring for use in selecting advertisements. Yet another applicationserver may be responsible for formatting and inserting advertisements ina video stream being transmitted to devices in the premises 102 and/orto the mobile devices 125. The local office 103 may comprise additionalservers, such as diagnostic server 122 (described below), additionalpush, content, and/or application servers, and/or other types ofservers. Although shown separately, the push server 105, the contentserver 106, the application server 107, the diagnostic server 122,and/or other server(s) may be combined. The servers 105, 106, 107, and122, and/or other servers, may be computing devices and may comprisememory storing data and also storing computer executable instructionsthat, when executed by one or more processors, cause the server(s) toperform steps described herein.

A premises 102 a may comprise an interface 120. The interface 120 maycomprise circuitry used to communicate via the communication links 101.The interface 120 may comprise a modem 110, which may comprisetransmitters and receivers used to communicate via the communicationlinks 101 with the local office 103. The modem 110 may comprise, forexample, a coaxial cable modem (for coaxial cable lines of thecommunication links 101), a fiber interface node (for fiber optic linesof the communication links 101), twisted-pair telephone modem, awireless transceiver, and/or any other desired modem device. One modemis shown in FIG. 1, but a plurality of modems operating in parallel maybe implemented within the interface 120. The interface 120 may comprisea gateway 111. The modem 110 may be connected to, or be a part of, thegateway 111. The gateway 111 may be a computing device that communicateswith the modem(s) 110 to allow one or more other devices in the premises102 a to communicate with the local office 103 and/or with other devicesbeyond the local office 103 (e.g., via the local office 103 and theexternal network(s) 109). The gateway 111 may comprise a set-top box(STB), digital video recorder (DVR), a digital transport adapter (DTA),a computer server, and/or any other desired computing device.

The gateway 111 may also comprise one or more local network interfacesto communicate, via one or more local networks, with devices in thepremises 102 a. Such devices may comprise, e.g., display devices 112(e.g., televisions), STBs or DVRs 113, personal computers 114, laptopcomputers 115, wireless devices 116 (e.g., wireless routers, wirelesslaptops, notebooks, tablets and netbooks, cordless phones (e.g., DigitalEnhanced Cordless Telephone—DECT phones), mobile phones, mobiletelevisions, personal digital assistants (PDA)), landline phones 117(e.g. Voice over Internet Protocol—VoIP phones), and any other desireddevices. Example types of local networks comprise Multimedia Over CoaxAlliance (MoCA) networks, Ethernet networks, networks communicating viaUniversal Serial Bus (USB) interfaces, wireless networks (e.g., IEEE802.11, IEEE 802.15, Bluetooth), networks communicating via in-premisespower lines, and others. The lines connecting the interface 120 with theother devices in the premises 102 a may represent wired or wirelessconnections, as may be appropriate for the type of local network used.One or more of the devices at the premises 102 a may be configured toprovide wireless communications channels (e.g., IEEE 802.11 channels) tocommunicate with one or more of the mobile devices 125, which may be on-or off-premises.

The mobile devices 125, one or more of the devices in the premises 102a, and/or other devices may receive, store, output, and/or otherwise useassets. An asset may comprise a video, a game, one or more images,software, audio, text, webpage(s), and/or other content.

FIG. 2 shows hardware elements of a computing device 200. The computingdevice 200 may be any of the computing devices shown in FIG. 1 (e.g.,the mobile devices 125, any of the devices shown in the premises 102 a,any of the devices shown in the local office 103, any of the wirelessaccess points 127, any devices with the external network 109) and anyother computing devices discussed herein (e.g., any of the user devices301-303, the computing device 308, the mobile device 701). The computingdevice 200 may comprise one or more processors 201, which may executeinstructions of a computer program to perform any of the functionsdescribed herein. The instructions may be stored in a read-only memory(ROM) 202, random access memory (RAM) 203, removable media 204 (e.g., aUSB drive, a compact disk (CD), a digital versatile disk (DVD)), and/orin any other type of computer-readable medium or memory. Instructionsmay also be stored in an attached (or internal) hard drive 205 or othertypes of storage media. The computing device 200 may comprise one ormore output devices, such as a display device 206 (e.g., an externaltelevision and/or other external or internal display device) and aspeaker 214, and may comprise one or more output device controllers 207,such as a video processor. One or more user input devices 208 maycomprise a remote control, a keyboard, a mouse, a touch screen (whichmay be integrated with the display device 206), microphone, etc. Thecomputing device 200 may also comprise one or more network interfaces,such as a network input/output (I/O) interface 210 (e.g., a networkcard) to communicate with an external network 209. The network I/Ointerface 210 may be a wired interface (e.g., electrical, RF (via coax),optical (via fiber)), a wireless interface, or a combination of the two.The network I/O interface 210 may comprise a modem configured tocommunicate via the external network 209. The external network 209 maycomprise the communication links 101 discussed above, the externalnetwork 109, an in-home network, a network provider's wireless, coaxial,fiber, or hybrid fiber/coaxial distribution system (e.g., a DOCSISnetwork), or any other desired network. The computing device 200 maycomprise a location-detecting device, such as a global positioningsystem (GPS) microprocessor 211, which may be configured to receive andprocess global positioning signals and determine, with possibleassistance from an external server and antenna, a geographic position ofthe computing device 200.

The computing device 200 may also comprise circuitry 221 configured toreceive and/or send communications via a power line network. A powercord 220 may be connectable to an outlet or other source of electricalpower so as to deliver a power signal (e.g., a 120 volt, 60 Hz ACsignal) to an internal battery supply and/or charger (not shown) of thecomputing device 200. The circuitry 221 may comprise a filter that candetect communication signals added to the power signal and carried via apower line. The circuitry 221 may also or alternatively comprise asignal generator to generate a communication signal and add thatcommunication signal to a power signal for transmission via a powerline.

Although FIG. 2 shows a hardware configuration, one or more of theelements of the computing device 200 may be implemented as software or acombination of hardware and software. Modifications may be made to add,remove, combine, divide, etc. components of the computing device 200.Additionally, the elements shown in FIG. 2 may be implemented usingbasic computing devices and components that have been configured toperform operations such as are described herein. For example, a memoryof the computing device 200 may store computer-executable instructionsthat, when executed by the processor 201 and/or one or more otherprocessors of the computing device 200, cause the computing device 200to perform one, some, or all of the operations described herein. Suchmemory and processor(s) may also or alternatively be implemented throughone or more Integrated Circuits (ICs). An IC may be, for example, amicroprocessor that accesses programming instructions or other datastored in a ROM and/or hardwired into the IC. For example, an IC maycomprise an Application Specific Integrated Circuit (ASIC) having gatesand/or other logic dedicated to the calculations and other operationsdescribed herein. An IC may perform some operations based on executionof programming instructions read from ROM or RAM, with other operationshardwired into gates or other logic. Further, an IC may be configured tooutput image data to a display buffer.

FIG. 3 shows an environment 300 including a diagnostics server 302 incommunication with a home, via a gateway 304. The diagnostics server 302may be similar to the diagnostic server 122 (FIG. 1), described above,such that the diagnostic server 302 may be located at the headend 103(FIG. 1). As described herein, the diagnostics server 302 maycommunicate with the gateway 304 via an access network using an accessnetwork protocol such as, for example, DOCSIS. DOCSIS technology (and/orother termination system technology) may be used to provide networkaccess to a home. At the home, communication may occur between clientdevices 306 and via a mesh network that uses a different networkprotocol such as, for example, MoCA. MoCA technology (and/or other localnetwork interfaces) may be used in home mesh networking forcommunications to and/or from home devices. MoCA, in someconfigurations, may allow bi-directional communications across coaxialand/or hybrid-fiber coaxial cable.

As shown in FIG. 3, DOCSIS and MoCA technology may be used together todeliver, e.g., over a network and to devices in a home/office, dataand/or media content. Gateway 304 may be used to translate the data fromthe DOCSIS standard to the MoCA standard and vice versa. For example, asshown in FIG. 3, data (e.g., media content) may first traverse up theDOCSIS stack 308. After arriving at the gateway 304, the data maytraverse down the DOCSIS stack 308. As the data traverses down theDOCSIS stack 308, the data may begin to traverse up the MoCA stack 310.After arriving at one or more of the client devices 306 of a homenetwork, the data may traverse down the MoCA stack 310. Data may betargeted to particular client devices 306 based on various mechanismssuch as an internet protocol (IP) address of the client device. Data maybe sent to and/or from each one of the client devices via, for example,a simple network management protocol (SNMP).

Not all client devices in a home network may be configured for MoCA. Forexample, a legacy device with DOCSIS embedded in the device may not haveMoCA configured for that device. In some such examples, firmware updatesmay be sent to the non-MoCA devices to include SNMP MIB support andenable MoCA configuration. Alternatives to the SNMP include, forexample, RFC 1213 or TR 69/181, which may also be utilized as a protocolstack for communication with devices proximate the home/office.

As shown in FIG. 3, the diagnostic server 302 may comprise a performancemanager 312, a Complimentary Cumulative Density Function (CCDF)generator 314, the comparator 316, and the key performance indicator(KPI) database 318. The performance manager 312, the CCDF generator 314,the comparator 316, and/or the KPI database 318 may have hardware and/orsoftware implementations. For example, the performance manager 312, theCCDF generator 314, the comparator 316, and/or the KPI database 318 maybe implemented via a computing device such as computing device 200 (FIG.2). Additionally or alternatively, the performance manager 312, the CCDFgenerator 314, the comparator 316, and/or the KPI database 318 may beimplemented, wholly or in-part, as software applications run bydiagnostics server 302.

The performance manager 312 of the diagnostic server 302 may perform themethods described herein and may remotely access, command, and monitorclient devices 306 remotely in order to make determinations about apremises network. The performance manager 312 may poll the clientdevices 306, via the SNMP, to receive information and/or request anoperation to be performed by the client device. The performance manager312 may access a management information base (MIB), which describesoperations, such as alternative channel assessment (ACA) operations thatmay be used to measure network characteristics of MoCA (or other networkprotocols) enabled client devices. The performance manager 312 maycollect data from a population of client device 306 and generate one ormore threshold reference values and/or functions. The performancemanager 312 may collect data from one or more client devices 306 of thepopulation of client device 306, for comparison against baseline data orthe generated one or more threshold reference values and/or functions.

The CCDF generator 314 may format data collected by the performancemanager 312 to generate one or more graphical functions to represent thecollected data in one or more different variations. For example, theCCDF generator 314 may create a histogram, a Cumulative Density Function(CDF), and/or a CCDF.

The example comparator 316 may be configured to compare measured data toone or more thresholds. The one or more thresholds may include datastored in key KPI database 318.

The KPI database 318 may store historical data, data received by theperformance manager 312, mathematical formulas, functions, standardperformance measurements, operations for client devices, point of entry(POE) filter profiles, and/or other data related to performance.

Measured performance characteristics may be compared againstcharacteristics of a population (e.g., all measured client devices) withrespect to the probability of occurrence. This may be helpful indetermining an average or standard in which to identify abnormalitiesand predict the frequency of identifying such abnormalities. Thesemeasurements may account for drift over time and may provide a moreaccurate diagnostic tool. For example, an average or standard that ismeasured at a first time may be different than an average or standardmeasured at a subsequent time. Accordingly, periodic populationmeasurement may be made to account for such possible variations.

FIG. 4A shows an example method 400 for generating a ComplimentaryCumulative Density Function. The method 400 may be performed bydiagnostic server 302 in the form of computer-readable instructionsstored in memory, such as, for example, ROM 202 and/or RAM 203.Performance characteristics may be collected from a plurality of clientdevices 306 from a plurality of home networks serviced by a provider ina given population (block 402). The collected performancecharacteristics may be formatted with respect to the probability ofoccurrence (block 404). The formatted performance characteristics may betranslated into a cumulative density function (block 406). Thecumulative density function may be generated by aggregating thefrequency of occurrence of a performance metric from zero to one. Asmore and more data is acquired, the cumulative density functionapproaches one. Accordingly, to better analyze the data, the complimentof the translated cumulative density function may be determined, therebycreating a CCDF (block 408). CCDF may format the data so that themajority of the population data is centered about the origin. At block410, the sample diagnostic data received via the SNMP may be compared toone or more threshold values.

FIGS. 4B-4D illustrate visual representations of the collected data atblocks 404, 406, and 408, respectively. FIG. 4B shows a graph 412 of theperformance characteristics after being collected from a population andformatted based on the probability of occurrence. In some such examples,the data forms a bell curve, as shown in FIG. 4B, where a majority ofthe data points lie in the middle having an average probability ofoccurrence, and where the data points at the high end and the low endare minorities having a low probability of occurrence. FIG. 4C shows agraph 414 of the performance characteristics after being translated intoa cumulative density function. FIG. 4D shows a graph 416 of theperformance characteristics after the complement has been determined.

Example CCDFs are shown and described with reference to FIGS. 5-6B. Withrespect to FIG. 5, a CCDF 500 is shown. The CCDF 500 comprises datacorresponding to measured performance. A threshold function 502 may begenerated based on population data 504 and may represent a populationnorm of a performance metric (e.g., downstream receive signal to noiseratio (SNR) for an entire population of cable modems).

As shown in FIG. 5, the X-axis may correspond to the quality of theperformance metric. The performance metric may be any KPI of interestand the quality may be a measure of “goodness” or how well a clientdevice is performing. In the shown example of FIG. 5, “goodness” mayimprove for values further away from the origin, while values close tothe origin may be less good. The directionality of “goodness” may resultin the greatest areas of the CCDF curve being near the origin. TheY-axis may correspond to the probability of that quality of performanceoccurring. For example, the Y-axis minimum may correspond to 0probability, and the Y-axis maximum may correspond to a probabilityvalue of 1 or 100%. Performance metrics very near zero have a lowlikelihood of occurrence. In contrast, performance metrics near 100%have a high likelihood of occurrence.

The performance manager 312 may collect first data 506 and second data508. The first data 506 may be received from a home network withfavorable home networking. The second data 508 may be received from ahome network where the end user has logged a ticket, citing unfavorablehome networking experience. The comparator 316 may determine, based onnumerous methods, that the first data 506 is better than the thresholdfunction 502. For example, it may be determined that the first data 506has values greater than the threshold function 502. Alternatively, thenumber of slope changes may be compared between the threshold function502 and the first data 506. Even further, image recognition software mayidentify that the first data 506 is further to the right than thethreshold function 502 is CCDF 500. Similarly, it may be determined thatthe second data 508 has values lower than the threshold function 502,has less number of slope changes than the threshold function 502, or isfurther to the left of the threshold function 502. Other data values maybe compared such as the slope, mean, standard deviation, median, etc.

FIG. 6A shows a graph 600 wherein the X-axis may represent a value ofthe aggregate KPI. For example, the X-axis may represent the radiofrequency (RF) electrical performance of a device (e.g., amplifier,splitter, etc.) output-port-to-output-port (OP2OP) isolation, indecibels (dBs). The Y-axis may represent the probability (P) associatedwith the device (e.g., drop amplifier or splitter) achieving a specificelectrical performance value.

A first threshold, μCOMP, may be determined to be the average ofperformance characteristics measured for all devices in a population.μCOMP may correspond to an average OP2OP isolation. σCOMP may correspondto a degree of reliability shown as the standard deviation. For example,99.9% of compatible devices may have an OP2OP isolation that may be ≤30dB.

With respect to FIG. 6B, products that meet a particular OP2OP isolationspecifications for compatible devices (e.g., drop amplifiers, splitters,etc.) may have a predictable or ideal contribution to OP2OP loss thatmay be indicated and/or described via the CCDF. Incompatible devices(e.g., drop amplifiers, splitters, etc.) may degrade both the mean andstandard deviation of the OP2OP isolation performance, which may beindicated via comparison with a minimum expected performance. Forexample, in graph 602, the performance characteristics associated withan incompatible device may have a poorer OP2OP mean isolation, resultingin a right shift of the CCDF function, and ultimately higher averagepath loss and worse home network performance. Additionally, the OP2OPstandard deviation may be poorer, resulting in a flattening out of theCCDF function, and ultimately a less reliable performing home networkdue to the higher degree of performance variation.

A measured mean and standard deviation of the OP2OP isolationperformance may be worse than threshold function 502. This may indicatepath loss and/or OP2OP isolation as a cause of lower performance. A homenetwork exhibiting such performance may be flagged for remediation. Theincompatible device function 604 shows such a case where both the meanand standard deviation are appreciably worse than a compatible devicefunction 606.

The excessive path loss condition can be shared with a technician, priorto arriving at the home, guiding the technician to investigate whetherincompatible splitter or drop amplifier exists within the home network.After replacement of the incompatible device with a compatible one,immediate feedback can be provided to the technician on whether pathloss falls below threshold or if the path loss problem persists. Theabove described process establishes thresholds for acceptable homenetworking path loss and indicates when path loss thresholds are notbeing met.

This process, and others described herein, may be used to diagnose homenetwork defects and/or deficiencies such as damaged cables, looseconnectors, incompatible drop amplifiers and splitters, VoIP portconnections, and missing point-of entry filters, and other hardwareand/or software issues.

Incompatible devices may detected based on splitter jumping (e.g., datatraversing between network nodes through a splitter). Splitter jumpingmay be considered to be an undesirable network trait in access networks.Accordingly, for access networks, the performance metric of OP2OPisolation has often been specified very high such as, for example, 25dB. MoCA signals can splitter jump at extremely high frequencies, whereattenuation may be much higher than what would be experienced withinDOCSIS frequency bands.

A method 700 for detecting incompatible devices is shown in FIG. 7.method 700 may be implemented by computer readable instructions storedin a tangible computer readable storage medium to be executed by amachine. method 700 begins at block 702, wherein a measurement may betaken, such as a frequency response scan across some or all clientdevices 306. For example, the mesh devices may be requested to performan operation and may measure the frequency response of each clientdevice 306. The measurement (M) may be a waveform, and thus, theperformance manager 312 may determine multiple values based on themeasurement M including, without limitation, the mean (μM), standarddeviation (σM), slope (mM), etc.

At block 704, for each client device 306, it may be determined whetherthe μM is greater than the μCOMP and whether the σM is greater than theσCOMP for the MoCA operating band. The MoCA operating band may begreater than the DOCSIS operating band and may be between 1100 MHz and1675 MHz. If it is determined that the μM is greater than the μCOMP andthat the σM is greater than the σCOMP for the MoCA operating band (block704: Y), then control proceeds to block 706. Otherwise (block 704: N),method 700 ceases operation.

At block 706, it may be determined whether the μM is greater than theμCOMP and whether the σM is greater than the σCOMP for a provideroperating band. The provider operating band may be a subset of the MoCAband and may be between 1125 MHz and 1175 MHz. If it is determined thatthe μM is greater than the μCOMP and that the σM is greater than theσCOMP for the provider operating band (block 706: Y), then controlproceeds to block 708. Otherwise (block 706: N), control proceeds toblock 710. At block 708, it may be indicated that the client device withthe μM greater than the μCOMP and the σM greater than the σCOMP for thehigher operating band has a high priority in a list of incompatibledevices. Control may proceed to block 710. At block 710, the clientdevice 306 may be identified as an incompatible device (e.g., dropamplifier, splitter, etc.) and the client device 306 may be flagged tobe replaced. Replacement of the incompatible device may be scheduledaccording to the priority in the list of incompatible devices.Thereafter, method 700 may cease operation.

For incompatible drop devices, the electrical performance may produce aunique and remotely detectable frequency response (e.g., a small band offrequencies which have extremely high loss: a suckout). A pre-definedminimum acceptable electrical performance threshold, based on compatibledrop amplifiers and splitters, may be used as a basis for comparison forthe home network's device(s) under test (DUT). Complete DUTcharacterization, combined with system noise, path loss, and OP2OPisolation may be accomplished using a variety of protocol specifictools, such as, for example, MoCA's Error Vector Magnitude (EVM) probe,which may produce complete frequency response data over MoCA's and MoCA2.0 operating bands. The frequency may be randomized.

Devices failing to meet MoCA 2.0 operating band standards may be flaggedfor immediate replacement, while devices that meet a provider'soperating band, but fail MoCA's operating band may be flagged as anissue for future bandwidth expansion and thus addressed at a later time.

Characterizations may be conducted across some and/or all DUT portcombinations, e.g. a four-way splitter may be a vector of OP2OPisolation values based on multiple two output port combinations for eachMoCA link, or 1:2 (output port 1 to output port 2), 1:3, 1:4, 2:1, 2:3,2:4, 3:1, 3:2, 3:4, 4:1, 4:2, 4:3. The port combinations may berandomized.

FIG. 8 shows a graphical representation 800 of suckout 802. If a suckoutis observed within a port combination of the DUT OP2OP EVM probemeasurements, then diagnostics server 302 may return an incompatiblenetwork device as a root cause for failure to meet a provider'spre-determined service criteria. Vendor products that meet the OP2OPisolation specifications for compatible drop amplifiers and splittersmay have a predictable and/or ideal contribution to OP2OP loss that mayalso be described via the CCDF.

The existence of point of entry filters may be determined. POE filtersare lowpass filters that may be installed in a MoCA network to isolatethe home network from neighboring home networks by attenuating the MoCAhome network signals at the home network's point of entry. Deployment ofwell-designed POE filters can prevent neighboring MoCA home networksfrom seeing each other and protect a MoCA home network from anyeavesdropping. Additionally, POE filters may be installed in a MoCAnetwork to improve connectivity with their 0 dB return loss in the MoCAfrequency band. MoCA home network signals that are incident on the POEfilter may reflect, with no loss contribution from the POE filter, backinto the home network, and may result in a stronger MoCA signal if thereflected path has lower loss than the original path.

POE filters are often installed to provide optimal MoCA home networksecurity and performance and are often located at a tap spigot,groundblock, or as close as possible point of the WAN side of a rootsplitter or drop amplifier input. POE filters aren't always included inall installations or are inadvertently removed by clients afterinstallation. Determining whether a POE filter is installed may beperformed prior to or during MoCA home network activation as apreemptive measure and provide real time feedback to an on-sitetechnician or client. Determining whether a POE filter is installed maybe performed after activation as a remedial measure. Various methods fordetecting POE filters in home networks may be utilized.

The diagnostics server 302 may detect the presence (or absence) of POEfilters, prior to a bridging event occurring, by analyzing a broadcastedcontinuous wave (CW) signal received by either a DOCSIS 3.1 or MoCAenabled CPE. CW signals may be used to perform RF alignment in accessnetworks. The continuous wave signal may be centered within, or slightlyabove, an access network (e.g., HFC) passband and, for example,broadcasted from a provider headend to one, some and/or all clients.Other continuous waves, which may be used for RF signal alignment, mayexist within a provider's downstream signal loading.

The access network may be designed to deliver 0 dBmV over frequency, ofall downstream signals, to all CPE, though some variation is expected.Downstream RF levels may range between +10 to −8 dBmV, based on, forexample, Broadband Recommended Installation Standards (BRIS). CWs usedfor RF alignment purposes may be maintained to be slightly higher thantheir service delivering signal counterparts.

A unique path loss (PL) associated with each client deployment may beused in determining the downstream RF receive level to client premiseequipment, which may vary with drop length and equipment used,including, for example, passive devices, amplifiers, and POE filters,within the home networks. FIGS. 9A-9C illustrate graphicalrepresentations of continuous wave signals and path loss acrossdifferent networks. For example, FIG. 9A shows a Fiber Deep network 900with DOCSIS 3.1 passband without POE. Fiber Deep networks (e.g., DOCSIS3.1) will likely exhibit the lowest PL because such networks may bedesigned to support up to 1,218 MHz pass band, with design budgetallowances for up to 200' of RG11 drop cable and four outlets, path lossshould be at its lowest below the start of the MoCA operating band, or1,125 MHz.

Above 1,125 MHz the POE lowpass filter response may attenuate signalscoming into or out of a home in order to isolate MoCA home networks fromone another. Therefore, the broadcasted CW receive level may beattenuated below the expected receive level range previously discussedby at least 40 dB or more, depending on the POE filter design. FIG. 9Ashows a home network with a missing POE filter. In such an example, thedownstream passband between 1,002 and 1,218 MHz may be used by DOCSIS3.1. When no MoCA POE filter is installed, the CW will pass through boththe access and home network with attenuation expected for all downstreamaccess network signals, and detectable by home CPE capable of operatingat 1,125 MHz (e.g., MoCA nodes).

FIG. 9B shows a Non Fiber Deep network 902 with DOCSIS Passband withoutPOE. Non Fiber Deep networks, with a downstream upper edge of 1,002 MHz,may exhibit more path loss than Fiber Deep, due to roll off experiencedabove 1,002 MHz, but may be less than POE enabled homes, 2 dB≤pathloss≤40 dB.

Correctly installed POE filters may exhibit more attenuation above thePOE filter cutoff range (e.g., 1,002 MHz). POE filters may provide astopband attenuation of at least 40 dB starting at 1,125 MHz. Acceptablemethods for missing POE filter detection may involve defining CWdownstream receive power as the KPI for the deployment population andestablishing known thresholds for this value when POE filters arecorrectly installed. Remote detection of the POE filter may be performedby first collecting an estimate from all MoCA nodes within a subscriberhome network, and determining whether the CW receive power is lower thana population threshold, for example −40 dBmV.

As described herein, MoCA SNMP MIB can be used to detect missing POEfilters. Through either analyzing channel characteristics includingripple or tilt responses, which are further described herein, or bydetecting broadcasted CWs in the access network, MoCA nodes may providevaluable information regarding the contribution of a correctly installedPOE filter.

FIG. 9C shows a Non Fiber Deep network 904 with DOCSIS Passband withPOE. POE enabled homes may exhibit higher path loss because of theinstalled POE filter, which provides a stopband attenuation of, forexample, at least 40 dB, path loss>>>40 dB. Thresholds may be adjustedslightly to account for statistical variations associated with specificimplementations, such as varying continuous wave center frequencies, andhome network technology deployments

FIG. 10 shows a method 1000 to identify whether a POE filter isinstalled in a home network. The example method 1000 begins at block1002, where a frequency response scan across some or all client premiseequipment may be initiated that is capable of capturing a firstfrequency band (e.g., 1,125 MHz continuous wave). A broadcast continuouswave center frequency may be selected such that path loss is minimizedand visibility among future DOCSIS 3.1 and/or current MoCA enableddevices is maintained. A broadcast continuous wave RF level may be usedthat it is aligned with the system loading, thereby resulting in anideal reception of 0 dBmV within the home of a client.

A continuous wave downstream receive power estimate may be collectedfrom a future DOCSIS 3.1 CM, and/or current MoCA node. DOCSIS 3.1 clientpremise equipment may support a downstream upper edge of 1,218 MHz, andmay be capable of estimating continuous wave power via Full Band Capture(FBC). MoCA client premise equipment may support a passband lower edgeof 1,125 MHz, and may be capable of estimating continuous wave power viasimilar FBC functionality.

At block 1004, it may be determined whether the continuous wave pathloss is between −40 dBmV and 40 dBmV. If it is determined that thecontinuous wave path loss is between −40 dBmV and 40 dBmV (block 1004:YES), then control proceeds to block 1006. At block 1006, an accountassociated with the home network in which the continuous wave path lossis measured between −40 dBmV and 40 may be indicated as not having a POEfilter installed. An alert signal, a non-compliance report may begenerated, and/or a request to install a POE for such a home may betransmitted. If it is determined that the continuous wave path loss isnot between −40 dBmV and 40 dBmV (greater than 40 dB or lower than −40dBmV) (block 1004: NO), then the home is flagged as having a POE filterinstalled. Thereafter, method 1000 ceases operation.

MoCA channel tilt may be measured. FIG. 11A shows a graphicalrepresentation of network 1100 including a HFC network tap 1102, a POEfilter 1104, an amplifier or splitter 1106, and a plurality of MoCAclient device nodes 1108 a, 1108 b, 1108 c, and 1108 d. The diagnosticsserver 302 may detect the presence (or absence) of POE filters, prior toa bridging event occurring, by analyzing channel estimates of currentgeneration MoCA enabled CPE.

MoCA client premise equipment in homes that have POE filters installedmay have channel responses that include a first (POE reflection) path1110 from a first MoCA client device node 1108 d through the amplifieror splitter 1106, to the POE filter 1104, back through the amplifier orsplitter 1106, and to a second MoCA client device node 1108 c, which maybe the dominant, or least loss path, and strongest when the POE isintegrated into a root device. A root device may be a MoCA compatibledrop amplifier or splitter, resulting in increased tilt from insertionloss instead of the OP2OP isolation. It may be beneficial todifferentiate between MoCA friendly drop amplifiers and splitters versusnon-MoCA friendly equivalents because any suckouts present in non-MoCAfriendly devices may corrupt the POE filter detection process.Accordingly, compatible home network devices with more predictable RFperformance throughout the MoCA band may be first identified beforeattempting to detect the presence of POE filters. If a POE filter ispresent, a confirmation signal may be generated and sent to centraloffice and/or field technicians. First path 1110 may result in increasedtilt from either the amplifier or passive device insertion loss.

MoCA client premise equipment installed in homes without POE filters maydepend largely on OP2OP isolation of the root device. A second path1112, from the first MoCA client device node 1108 d, to the amplifier orsplitter 1106, and to the second MoCA client device node 1108 c. Secondpath 1112 may be a flat loss over frequency for MoCA-friendly amplifiersand passives. This may result in less tilt than first path 1110.

Detecting installed POE filters may include analyzing the forward tiltobserved across MoCA devices. The approach may be similar to CCDFapproaches, but the tilt analysis may aggregate tilt measurements into aCCDF and compare those measured CCDFs to an established tilt thresholdCCDF that is associated with installed POE filters. Thresholdcomparisons may be used to decide whether or not a POE filter has beeninstalled by detecting the additional tilt, from a root device andcable, associated with the MoCA signals traversing the first path 1110.Threshold CCDF may be based on population measurements of POE filteredMoCA home networks and may converge to a minimum tilt value, where tiltis defined as the approximate linear variation over frequency across theMoCA operating band.

FIG. 11B shows a graphical representation 1114 of a referenceperformance characteristic 1116 and a measured performancecharacteristic 1118. As shown in FIG. 11B, the measured performancecharacteristic 1118 corresponds to a network not comprising a POEfilter. This methodology is utilized by the diagnostic server 302described herein to identify home networks lacking POE filters and othernetwork structure and performance impairments. Attenuation increaseswith frequency in the insertion loss of coaxial cable, drop amplifiers,and splitters. Vendors design drop amplifiers and splitters to meet aconstant OP2OP isolation value across frequency. Such attenuation overfrequency details may enable cable operators to distinguish whether anygiven network may be dominated by OP2OP isolation or insertion loss,where a CCDF observation with less forward tilt may reveal its averagetilt to be lower than the population threshold.

FIG. 12 shows a method 1200 to determine whether a POE filter isinstalled in a home network. method 1200 begins at block 1202, where itis determined whether the MoCA network contains service-impactingimpairments. FIG. 18 describes a determination as to whether the MoCAnetwork contains service-impacting impairments. If it is determined thatthe MoCA network contains service-impacting impairments (block 1202:YES), then method 1200 ceases operation. If it is determined that theMoCA network does not contain service-impacting impairments (block 1202:NO), control proceeds to block 1204.

At block 1204, it may be determined whether the MoCA network containsany incompatible devices. FIG. 7 describes a determination as to whetherthe MoCA network contains any incompatible devices. If it is determinedthat the MoCA network contains any incompatible devices (block 1204:YES), then an noncompliance report may be generated and method 1200ceases operation. If it is determined that the MoCA network does notcontain any incompatible devices (block 1204: NO), control proceeds toblock 1206.

At block 1206, channel tilt is measured based on the channel estimatesof all MoCA enabled devices. The tilt measurements may be aggregatedinto a single CCDF curve representing tilt probability. measured tiltstatistics may be compared to the tilt statistics associated to areference threshold (4 dB) representing POE reflected path tilt. Tiltstatistics may be stored in the KPI database 318.

At block 1208, it may be determined whether measured tilt is less than areference threshold. For example, it may be determined whether the meanmeasurement (μM) is greater than the mean reference (μR) and thestandard deviation of the measurement (σM) is less than the standarddeviation of the reference (σR) for the MoCA tilt.

If it is determined that μM≤μR or σM≥σR (block 1208: NO), then controlproceeds to block 1210. At block 1210, it may be determined that a POEfilter is not installed in the home network. If a POE filter is notpresent, an alert signal may be generated and sent to central officeand/or field technicians. The method 1200 may cease operation.

If it is determined that μM>μR and σM<σR (block 1208: YES), then controlproceeds to block 1212. At block 1212, it may be determined that a POEfilter is installed in the home network. If a POE filter is present, aconfirmation signal may be generated and sent to central office and/orfield technicians The method 1200 may cease operation.

Echoes may be detected in access networks and may be indicative of thepresence of a POE filter. Specifically, because a POE filter comprisesapproximately 0 dB return loss, the POE filter will introduce additionalMoCA signal propagation paths, whose net effect on the channel responsewill be a ripple, graphically represented as a magnitudinal spikeexceeding a threshold. A ripple may be considered to be a negativefeature in the Proactive Network Maintenance (PNM) mindset, becauseechoes in access networks usually means pairs of damaged or defectiveHFC components contributing to the generation of echoes.

Echoes in the MoCA home network may useful for detecting the existenceof a POE filter. Detection of echoes within the MoCA home network mayindicate that a POE filter has been successfully included in the MoCAhome network activation. A POE filter MoCA channel response may have anappreciable ripple, while a MoCA channel response without a POE resultsin a much flatter channel response.

A POE in the home RF network may introduce a large spike or ripple thatmay be noticeable in the CE domain, more clearly than in a Frequencydomain with granularity. Additionally, one may use Spectrum analysis(SA) and use magnitude values perform Fourier analysis (FA) to determinethe “ripple” effect of an echo. FIG. 13 is a graphical representation ofa MoCA system 1300 with a POE filter 1302 and an echo response 1304between the POE filter 1302 and a MoCA enabled device 1306.

Fourier analysis may be utilized (e.g., by the diagnostics server 302)to acquire a frequency response and analyze the CE domain. Fourieranalysis may utilize real values (e.g., Real²+Imaginary²) for asubcarrier magnitude. Fourier analysis may utilize complex Amplitude(Real)+Phase (Imaginary). The CE may provide the real-imaginary (RI)correction for individual subcarriers. Obtaining the RI may provide atime domain representation of the OFDM.

FIG. 14A shows and example DOCSIS 3.1 CE 1400. As shown in FIG. 14B, afirst echo length 1402 may be determined by measuring between a firstpeak 1404 and a second peak 1406 of the CE 1400. echo length 1402 maycorrespond to a short echo. In contrast, FIG. 14C shows a long echohaving a second echo length 1408 between a third peak 1410 and a fourthpeak 1412.

FIGS. 15A-15B illustrate an inverse fast Fourier transform (IFFT) 1500of the channel estimation data 1400 described with reference to FIG. 14Ato obtain a frequency response. As shown in FIG. 15B, IFFT 1500 shows afirst echo 1502 (e.g., a short echo) and a second echo 1504 (e.g., along echo). With this data, the relative expected response may becalculated when no POE is present.

FIG. 16 shows a method 1600 to identify whether a POE filter isinstalled in a home network. method 1600 begins at block 1602, wherechannel estimation complex data (CE-CD) may be collected for the homenetwork. Channel estimation (CE) may play an important part in anorthogonal frequency division multiplexing (OFDM) system. CE may be usedfor increasing the capacity of orthogonal frequency division multipleaccess (OFDMA) systems by improving the system performance in terms ofbit error rate. To facilitate the estimation of the channelcharacteristics, OFDM systems such as, for example, DOCSIS, Wi-Fi, longterm evolution (LTE), and MoCA, use reference or pilot symbols insertedin both time and frequency. Pilot symbols may provide an estimate of thechannel at given locations within a symbol time.

At optional block 1604, the reciprocal or compliment of the complex datamay be determined. At block 1606, an inverse fast Fourier transform maybe performed on the channel estimation complex data. At block 1608, themaximum peaks may be determined for the IFFT determined at block 1606.At block 1610, the echo length from 0 Hz to its peak value may bedetermined. For example, a first echo length may be determined, such asthe one identified in FIG. 14B, which may be associated with the firstecho 1502 indicated in FIG. 15B. At block 1612, the first echo lengthmay be recorded in the KPI database 318. At block 1614, it may bedetermined whether there are any additional peaks, such as, for example,the second echo 1504 indicated in FIG. 15B. If it is determined thatthere are additional peaks (block 1614: YES), control returns to block1610. If it is determined that there are no additional peaks (block1614: NO), control proceeds to block 1616.

At block 1616, the recorded echo lengths may be compared to a POEprofile. The POE profile may be stored within the KPI database 318. Atblock 1618, it may be determined whether a recorded echo length matches(e.g., is identical to or substantially similar to) the POE profile. Ifit is determined that the recorded echo length matches the POE profile(block 1618: YES), control proceeds to block 1620. At block 1620, it maybe determined that the home network does have a POE filter. If a POEfilter is present, a confirmation signal may be generated and sent tocentral office and/or field technicians. The method 1600 may ceaseoperation.

If it is determined that a recorded echo length does match the POEprofile (block 1618: NO), control proceeds to block 1622. At block 1622,it may be determined that the home network does not have a POE filter.An alert signal or noncompliance report may be generated in response todetermining that the home network does not have a POE filter. The method1600 may cease operation.

FIG. 17 shows an environment 1700 comprising a plurality of DOCSIS 3.1(D3.1) or Full Duplex DOCSIS devices (FDX) 1702, and their correspondingchannel estimation reflections 1704 prior to reaching a serving group1706. FIG. 18 shows a method 1800 to identify reflection points in ahome network. method 1800 may have a high probability to determine thedistance from the source device to the reflection point. A reflectionpoint in the HFC network may introduce a “ripple” effect that may bemore noticeable in the CE domain than in a Frequency domain withgranularity.

The example method 1800 begins at block 1802, where CE-CD may becollected for the home network. At optional block 1804, the reciprocalor compliment may be take of the complex data. At block 1806, an inversefast Fourier transform may be performed on the channel estimationcomplex data. At block 1808, the maximum peaks may be determined for theIFFT determined at block 1806. At block 1810, the echo length may bedetermined from 0 Hz to its peak value. At block 1812, the first echolength maybe recorded in the KPI database 318. At block 1814, it may bedetermined whether there are any additional peaks. If it is determinedthat there are additional peaks (block 1814: YES), control returns toblock 1810. If it is determined that there are no additional peaks(block 1814: NO), control proceeds to block 1816.

At block 1816, it may be determined whether there is another cable modemin the service group. If it is determined that there is another cablemodem in the service group (block 1816: YES), control returns to block1802. Otherwise (block 1816: NO), data analysis is performed on therecorded echo lengths. The aforementioned data analysis may be similarto the data analysis described in reference to blocks 1616-1622 withrespect to FIG. 16. Alternate echo detection algorithms may be utilizedin connection with the aforementioned data analysis.

The aforementioned data analysis may include determining magnituderesponses (MR) and reflection points (RP) as shown in graphicalrepresentations of FIGS. 19A-19F. For example, performing an IFFT of therespective CEs may be useful in identifying the RP. FIGS. 19A-19Fillustrate multiple examples of multiple CEs with varying echo delaysand their corresponding IFFT waveforms.

Self-Install Kits (SIK) allow optimized deployment and operations.Deployed devices may be capable of remotely changing their diplex filterconfiguration from a standard split diplex to a midsplit diplex filter.The standard split diplex filter may support a return path of 5-42 MHz,or approximately a 30 Mbps upstream speed service tier. The midsplitdiplex filter may support an expanded return path, or 5-85 MHz, whichmay enable provision of approximately 100 Mbps (or higher) upstreamspeed service tier to clients. Midsplit SIK success may depend on aprovider's ability to qualify client home networks for enhanced capacityservices.

A provider, via its installation practices, craftsmanship, and homenetwork product performance may have a path loss, of at least 25 dB,between a first device and any video device. Use of many third partyhome network devices may result in a much lower path loss. Midsplittransmissions within the 54-85 MHz band can disrupt existing videoservices when the transmission power is approximately 20 dB higher thanthe video signal receive power, at the set top box receiver. Highertransmission power may cause many devices to become nonlinear via aphenomenon known as Adjacent Channel Interference (ACI) or more commonlyknown as ACI Susceptibility.

FIG. 20 shows a method 2000 to remotely determine whether a clientassociated with a home network is qualified for Midsplit SIK. Examplemethod 2000 may enable Midsplit SIK assessments by combining DOCSIStelemetry metrics with an intra-client premise equipment path losscharacterization enabled by MoCA channel estimation. method 2000 beginsat block 2002, where it may be determined whether the MoCA networkcontains service-impacting impairments. FIG. 18 shows a determination asto whether the MoCA network contains service-impacting impairments.

If it is determined that the MoCA network does not containservice-impacting impairments (block 2002: NO), then control proceeds toblock 2004. At block 2004, it may be determined whether the MoCA networkcontains any incompatible devices. FIG. 7 shows a determination as towhether the MoCA network contains any incompatible devices. If it isdetermined that the MoCA network does not contain any incompatibledevices (block 2004: NO), control proceeds to block 2006.

At block 2006, the MoCA band path loss may be measured. For example, theMoCA band path loss may be measured from a first device of the homenetwork to a second device of the home network (e.g., an XB6 device toan XG1 device). Using passive and cable loss specifications overfrequency, the MoCA band (1,125-1,675 MHz) path loss to midsplit bandpath loss may be extrapolated.

At block 2008, midsplit band path loss may be estimated based on knownequipment specifications. At block 2010, the first device may be queriedfor maximum transmit power and transmit channel set. A 57 dBmV maximumDOCSIS transmit power may be presumed across 6 single carrier quadratureamplitude modulation (QAM) signals, or a 56 dBmV maximum DOCSIS transmitpower across 8 single carrier QAM signals. At block 2012, the seconddevice may be queried for minimum receive power.

At block 2014, it may be determined whether the transmit powerdetermined at block 2010 minus the estimated midsplit band path lossdetermined at block 2008 minus the receive power determined at block2012 is less than or equal to a first threshold (e.g., 20 decibels). Ifit is determined that the transmit power determined at block 2010 minusthe estimated midsplit band path loss determined at block 2008 minus thereceive power determined at block 2012 is greater than the firstthreshold (block 2015: NO), if it is determined that the MoCA networkcontains service-impacting impairments (block 2002: YES), or if it isdetermined that the MoCA network contains an incompatible device (block2004: YES), then control proceeds to block 2016. At block 2016, it maybe determined that the client associated with the home network includingthe first and second devices is disqualified as a midsplit SIKcandidate. The method 2000 may cease operation.

If it is determined that the transmit power determined at block 2010minus the estimated midsplit band path loss determined at block 2008minus the receive power determined at block 2012 is less than or equalto the first threshold (block 2015: YES), control proceeds to block2018. As block 2018, it may be determined that the client associatedwith the home network including the first and second devices isqualified as a midsplit SIK candidate. The method 2000 may ceaseoperation.

Path loss estimates between client devices (e.g., a cable modem and settop boxes) may be determined in order to remotely assess whether aclient may participate in midsplit self-install. Path loss estimates maybe collected for the MoCA operating band using MoCA 2.0 SNMP MIB dataassociated with the ACA OIDs.

Path loss for the midsplit operating band may be estimated, based on adetermination that no incompatible devices were detected in the MoCAhome network using MoCA band path loss estimates combined with knownproduct specifications for drop amplifiers, splitters, and drop cable.If a cable operator were to measure 20 dBmV receive level from a ACA EVMprobe, then subtracting that value from the known transmit level of55.75 dBmV may result in a MoCA path loss of approximately 35.75 dB. Ifa POE filter was detected and 4-way MoCA-friendly splitter was used,then the path loss may be dominated by the splitter's IL and not OP2OPisolation. A MoCA-friendly IL for the MoCA operating band may be 11.5dB, and 4 dB less, or 7 dB, for midsplit operating band. 11.5 dB may bededucted twice from the path loss, because the MoCA signal may passthrough the splitter twice when a POE filter is installed. It may beassumed that the remaining 12.75 dB comes from cable attenuation. RG6drop cable has approximately 8 dB loss per 100 ft, in the MoCA band,resulting in an equivalent RG6 cable length of approximately 155 ft,Estimating the midsplit band loss, based on 2 dB loss per 100 ft, forthe same length of cable may result in approximately 3 dB of cableattenuation. 3 dB of cable attenuation may be an estimated value and maynot be a true representation of cable loss for a variety of reasons, forexample, the POE filter and the root splitter may not be co-located.Additionally, the equivalent midsplit attenuation may be based on theOP2OP isolation of the splitter (e.g., 25 dB) because the midsplitsignals may not reflect off the POE filter. Therefore, the midsplit pathloss estimate may be 25 dB+3 dB=28 dB.

Whether there will be an ACI Susceptibility issue when activating amidsplit service may be estimate based on, for example, the midsplitpath loss. The estimation may comprise querying the CM for its maximumtransmit power or obtaining its maximum transmit power from themanufacturer specifications. A maximum upstream transmit power may be 57dBmV per 6.4 MHz. The estimation may further comprise querying for aminimum set top box receive power (e.g., 0 dBmV per 6 MHz). Adesired-to-undesired signal ratio (D/U) may then be estimated with thedownstream set top signal being the desired signal, and the CM upstreamtransmit signal being the undesired signal according to equation 1.

$\begin{matrix}{{{Set}\mspace{14mu} {top}\mspace{14mu} {Box}\mspace{14mu} {Desired}\mspace{14mu} {to}\mspace{14mu} {Undesired}\mspace{14mu} {Ratio}\mspace{14mu} {Estimate}\mspace{14mu} \left( {D\text{/}U} \right)}{\frac{D}{U} = {\left\lbrack {{U\left( {{Upstream}\mspace{14mu} {Transmat}\mspace{11mu} {Power}} \right)} - {D\left( {{Downstream}\mspace{14mu} {Receive}\mspace{14mu} {Power}} \right)}} \right\rbrack - {{Midsplit}\mspace{14mu} {PL}}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

A D/U may be 57 dBmV−0 dBmV−28 dB=29 dB. Since the D/U is 9 dB higherthan the previously discussed threshold of 20 dB, the home network mayrequire remediation in order to support midsplit based services.Accordingly, this home network would not qualify for a SIK. Remediationmay involve improving the isolation between the CM and set top boxeswith enhanced isolation splitters providing OP2OP isolation of ≥35 dB orvia notch filters, whose stop band attenuation may add ≥48 dB isolation.Both of the remediation approaches discussed would likely require fieldtechnician support.

ACA may be analyzed to identify areas of the occupied OFDM spectrum thatare highly attenuated. This may result in a cost of lower subcarriermodulations, which may potentially lower the PHY/MAC rate link. Cableoperators are migrating to MoCA-friendly Splitters (MFS). MFS may bereserved for clients that may have physical (PHY) layer issues. The MoCA2.0 ACA feature may assist a customer account executive (CAE) or fieldtechnician in determining why the client is experiencing a PHY layer oran IP/Network connectivity issue.

A correction factor may be added so that the ACA response is analogousto a spectrum analyzer. The following example may involve a 4-waynon-MoCA-friendly splitter (NMFS) where the ExD MoCA channel frequencyresponse is not a flat spectral response.

A flow chart 2100 for retrieving the ACA Error Vector Magnitude (EVM)probe data is shown and described with reference to FIG. 21. An ACA EVMprobe may be performed with respect to each of the MoCA enabled devices.

At block 2102, an EVM probe may be initiated between a first device(e.g., MoCA-X) and a second device (e.g., MoCA-Y). For example, thefirst device may be assigned to send an EVM Probe to the second device.When initiating an EVM Probe between two MoCA endpoints, the MoCAnetwork may be quiet, except for the two MoCA devices being evaluated.An EVM Probe may be performed from Channel D1 through D10, skipping theodd D-Channels (e.g., D3, D5, D7 and D9) to prevent an overlappingspectrum. All channels may be used to implement an overlapping spectrum.

The EVM probe data may be normalized. When graphing the power levelsover frequency, the EVM probe may appear to be a flat response, withpossible oscillation, or ripples across the OFDMs, as shown in FIG. 25.A correction of the EVM probe data may be performed to get a betterrepresentation of the actual OP2OP isolation response. To normalize theEVM probe data, the actual measured 100 MHz receive power may bedetermined in dBm, including OFDM guard bands at the F-Connector(ACATotalRxPower) and the per-subcarrier processed receive EVM in dBm(ACAPowerProfile).

The ACAPowerProfile OFDM subcarrier dBm EVM measurement may beintegrated to calculate its total channel power according to equation 2below:

$\begin{matrix}{{{EVM}_{CPCalc}{dBm}} = {10*{\log_{10}\left( {\sum\limits_{n = 0}^{{{EVMBin}.{size}} - 1}\left\lbrack 10^{\frac{{EVMBin}{(n)}}{10}} \right\rbrack} \right)}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

wherein:

-   EVMBin=The stored array of EVM Probe dBm levels from index    SubCarrer₀ to index SubCarrier_(N-1)-   EVM_(CPCalc) in dBm=The Total Channel Power calculated from the EVM    Probe Data

The correction offset may be calculated according to equation 3 below:

if EVM_(CPCalc)dBm<ACATotalRxPower

EVM_(Offset)=|EVM_(CPCalc)dBm+ACATotalRxPower dBm|

else if EVM_(CPCalc)dBm>ACATotalRxPower

EVM_(offset)dBm=|EVM_(CPCalc)dBm−ACATotalRxPower dBm|  Equation 3

A correction offset may be applied to normalize the EVM Probe Dataaccording to equation 4:

-   -   Iterate all subcarriers, i=0 . . . N

EVMBin_(Correction)(i)=EVMBin(i)+EVM_(Offset) dBm  Equation 4

If the above results are in dBm, the results may be converted to dBmVaccording to equation 5:

$\begin{matrix}{{dBmV} = {{10*{{Log}_{10}\left( \frac{75\Omega}{1*10^{- 3}} \right)}} + {dBm}}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

FIG. 26 shows the normalized EVM probe. FIGS. 27-30 represent the PTPEVM probe normalize responses. At block 2104 of FIG. 21, ACA data may berecorded. At block 2106, it may be determined whether to perform areverse ACA EVM probe for detecting path loss asymmetry, which may be acondition associated with older generation, non-MoCA-friendly dropamplifiers, such as when using VoIP/passive port connections. ReverseACA EVM probes may have a more precise average of the path loss becausethere may be differences among bidirectional paths. If it is determinedto perform the reverse ACA EVM probe (block 2106: YES), then a reverseACA EVM probe may be performed and control may return to block 2102. Ifit is determined to perform the reverse ACA EVM probe (block 2106: NO),then the reverse ACA EVM probe may be ignored and control proceeds toblock 2108.

At block 2108, it may be determined whether there is another MoCAdevice. If it is determined that there is another MoCA device (block2108: YES), then control proceeds to optional block 2110. At optionalblock 2110, a wait time (e.g., 10-15 seconds) may be added prior toreturning to block 2102. If it is determined that there are no otherMoCA devices (block 2108: NO), then process 2100 ceases operation.

FIG. 22 is a graph illustrating OP2OP isolation of MoCA ExD OFDM BeaconSignals from channels D1 thru D10. The shown example of FIG. 22demonstrates the amount of attenuation loss a MoCA channel mayexperience. FIG. 22 may represent the summation of MoCA beacon used toestablish a MoCA Network Link. FIG. 23 is a topology diagramillustrating a first network topology (e.g., loop-through) on the leftand a second network topology (e.g., home-run) on the right.

FIG. 24 shows a graph of not normalized SubCarrier Power. FIG. 25 showsa normalized SubCarrior Power. FIG. 25 may be used to identify theeffects of the MoCA RF communication channel based on tilt. This MoCAcommunication channel of FIGS. 24-25 may be dominated by OP2OPisolation, because there may be significant reverse tilt across the MoCAband. Reverse tilt, as shown in FIG. 25, may not be enough to diagnoseincompatible splitters, because vendors may apply processing tocompensate for the tilting effects observed in OP2OP isolation profiles.

The PTP EVM probe normalized responses may indicate an incompatiblesplitter for MoCA, and may represent some flatness after starting withD1. FIG. 26 may correspond to MoCA-1 PTP EVM Probe Overlay to MoCA-2, 3and 4. FIG. 27 may correspond to MoCA-2 PTP EVM Probe Overlay to MoCA-1,3 and 4. FIG. 28 may correspond to PTP EVM Probe Overlay to MoCA-1, 2and 4. FIG. 29 may correspond to MoCA-4 PTP EVM Probe Overlay to MoCA-1,2 and 4. FIG. 30 may correspond to PTP EVM Probe Showing HPF Edge,comprising a highly attenuated signal below D1. For example, theresponse in FIG. 30 may correspond to the lower edge of a high-passfilter (HPF), which allows only D1-D10 channel and rejects allfrequencies below 1,150 MHz internal of the MoCA embedded device.

FIG. 31 is a system diagram illustrating physical wiring of home network3200 as described herein. FIG. 32 is a graphical representation of anormalized MoCA RF channel response with and without a correctlyinstalled POE filter, which may correspond to an EVM probe PTP viewpointwith/without POE.

FIGS. 33-56 further describe various remote evaluation testingtechniques in determining the MoCA in-home health assessment related toincompatible drop amplifiers, splitters (which may Non-MoCA Friendly(NMF)), and missing Point-of-Entry (POE) filters in addition to, basedon, or as alternatives of the foregoing. If incompatible dropamplifiers, splitters (which may Non-MoCA Friendly (NMF)), or missingPoint-of-Entry (POE) filters are identified, an alert signal ornoncompliance report may be generated. The alert signal or noncompliancereport may be sent to the central office and/or to field technicians.Alternate Channel Assessment (ACA) and Mesh Sub-Carrier Modulation(MeshScMod) techniques are described below. MoCA PNM data may beretrieved via SNMPv2 (or SNMPv3), WebPA and RDK Telemetry.

There are two types of ACA operations: Alternate Channel Quiet LineAssessment and Alternate Channel EVM Probe Assessment. One or more MoCAnodes (e.g., within homes and/or offices) may perform the AlternateChannel Quiet Line Assessment. At least two nodes may perform theAlternate Channel EVM Probe Assessment operation.

MoCA MeshScMod may include 512 subcarriers, where 32 of the subcarriersmay be used in a guard band. The total number of usable sub-carriers maybe 480. MoCA MeshScMod may include three types of bit-load profiles:Very Low Packet Error Rate (VLPER), Nominal Packet Error Rate (NPER) andstandard ScMod.

The performance manager 312 may perform the below example CLI procedurefor operating and obtaining the ACA via SNMP.

TABLE 3 ACA EVM | Quiet SNMP Calls Source NodeID for EVM Probe snmpset-v 2c -c <RW_String> -m all -M < MIB-DIR 0-15 > udp6:<IPv6>mocaIfAcaNodeID.1 i <NodeID> 0 = Quiet snmpset -v 2c -c <RW_String> -mall -M < MIB-DIR 1 = EVM > udp6:<IPv6> mocaIfAcaType.1 i<ACA_Probe_Type> 1150 MHz = MoCA Chan <46> snmpset -v 2c -c <RW_String>-m all -M <MIB-DIR> 1150 MHz/25 MHz = 46 udp6:<IPv6> mocaIfAcaChannel.1u <MoCA_Channel> BitMask Convert to Binary snmpset -v 2c -c <RW_String>-m all -M <MIB-DIR> 2NodeID = BitMask udp6:<IPv6>mocaIfAcaReportNodeMask.1 x “<00 00 0000000000000001 = NodeID: 0 00 01>”Reporting HEX: 00 00 00 01 0000000000000010 = NodeID: 1 Reporting HEX:00 00 00 02 0000000000000100 = NodeID: 2 Reporting HEX: 00 00 00 04 • •1000000000000000 = NodeID: 15 Reporting HEX: 80 00 00 00Multi-Report-Node 000000000001001 = NodeID 0 + 3 00 00 00 09 = HEX(NodeID: 0 + 3) snmpset -v 2c -c <RW_String> -m all -M <MIB-DIR>udp6:<IPv6> mocaIfAcaInitiate.1 i <1 = Start_ACA_Process> Poll untilsuccess(0) snmpwalk -v 2c -c <RW_String> -m all -M <MIB- DIR>udp6:<IPv6> mocaIfAcaStatus.1 snmpwalk -v 2c -c <RW_String> -m all -M<MIB- DIR> udp6:<IPv6> MocaIfAcaEntry

TABLE 4 MoCAIfAcaEnry SNMP Call snmpwalk -v 2c c <RW_CommString> -m all-M <MIB-DIR> udp6:<IPv6> MocaIfAcaEntry MOCA20-MIB::mocaIfAcaNodeID.1 =Gauge32: 1 MOCA20-MIB::mocaIfAcaChannel.1 = Gauge32: 46MOCA20-MIB::mocaIfAcaReportNodeMask.1 = BITS: 00 00 00 01 31MOCA20-MIB::mocaIfAcaInitiate.1 = INTEGER: 0 MocaAcaStatusMOCA20-MIB::mocaIfAcaStatus.1 = INTEGER: success(0) DESCRIPTION“Represents the status of the last Aca probe” SYNTAX INTEGER { success(0), fail- BADCHANNEL (1), fail- NOEVMPROBE (2), fail (3), in-PROGRESS(4) } Result in dBm MOCA20-MIB::mocaIfAcaTotalRxPower.1 = INTEGER: -8Two's Compliment MOCA20-MIB::mocaIfAcaPowerProfile.1 = Hex-STRING: 2F 2FRepresentation. 2E 2F 2E 2D 2E 2D 2B 2B 2B 26 21 1C 1B 1B1C 1B 1B 1C 1AEach return value is 1A 1C 1A 1B 1B 1A 1B 1B 1D 1C 1A 1A 1B 1B 1A 1B 1A1A 19 positive, but the actual 1A 1B 1B 1B 19 1B 19 1A1B 1A 18 1A 1A 1A1B 1A 1A 1A 19 value is negative. 19 19 19 1B 1A 1B 1A 1A 1A 1A 1A 1B 1A1A 1A 1B 1A 1A 1A Implementer requires to 1A 1A1B 1B 1A 1A 1C 1B 1C 1B1B 1B 1B 1B 1B 1B 1C 1D 1C multiply by -1 1C 1B 1C 1B 1B 1C 1B 1C 1C 1B1A 1B 1B 1C 1D1C 1C 1B 1B 0x2F = 47(-1) = -47 dBm 1C 1C 1C 1B 1C 1B 1C1D 1C 1D 1C 1B 1C 1C 1B 1D 1C 1B 1B 1D 1C 1B 1C 1C 1B 1C 1D 1B 1A 1C 1D1C 1C 1C 1C 1C 1D 1D 1C 1D 1B 1D 1D 1C 1D 1D 1C 1D 1C 1C 1D 1C 1C 1D 1D1D 1C 1D 1D 1C 1E 1C 1C 1C 1D 1D 1D 1D 1C 1C 1D 1C 1D 1C 1D 1E 1C 1D 1C1C 1C 1E 1C 1C 1C 1D 1D 1E 1C 1D 1D 1C 1D 1D 1C 1D 1D 1E 1D 1C 1C 1D 1C1C 1C 1C 1E 1C 1D 1C 1C 1C 1D 1C 1B 1D 1D 1D 1C 1B 1D 1C 1E 1D 1D 1D 1D1E 1D 1D 1E 1E 1E 1C 1D 1D 22 24 28 2C 26 2C 2A 27 1F 1D 1D 1E 1E 1E 1D1D 1C 1D 1C 1D 1E 1D 1C 1C 1D 1C 1B 1E 1B 1B 1C 1B 1D 1C 1C 1C 1C 1C 1D1C 1D 1C 1E 1D 1C 1C 1D 1C 1C 1C 1C 1D 1C 1C 1E 1C 1C 1B 1B 1F 1C 1C 1C1C 1B 1C 1C 1C 1C 1D 1C 1D 1A 1D 1E 1D 1E 1C 1D 1D 1D 1C 1C 1B 1C 1D 1E1A 1C 1A 1B 1B 1D 1C 1B 1D 1B 1B 1D 1C 1D 1D 1C 1D 1A 1C 1D 1C 1B 1D 1D1D 1C 1B 1D 1C 1C 1C 1D 1C 1A 1A 1C 1C 1C 1C 1D 1C 1D 1C 1A 1C 1C 1C 1D1C 1A 1B 1B 1D 1B 1A 1B 1B 1C 1C 1B 1C 1A 1B 1C 1C 1B 1A 1B 1B 1A 1A 1B1B 1A 1B 1B 1C 1A 1A 1A 1A 1A 1B 1A 1B 1A 1A 1A 1B 1A 1A 1A 1A 1B 1A 1A1A 1A 19 1A 1B 1A 1A 1A 1A 1C 19 1A 1A 1A 19 1A 19 19 1A 19 1A 1A 1A 1919 19 1A 1B 19 1A 19 1A 1A 19 1A 19 18 1A 19 1A 1A 19 1A 19 19 1A 1A 1A1A 19 19 1A 1A 1B 19 19 18 1A 1A 1A 1A 1A 1B 1A 1C 20 25 2A 29 2A 2C 2C2C 2E 2E 2E 2E

The below procedure may be performed for operating and obtaining theMeshScMod.

TABLE 5 Sub Carrier Modulation SNMP Calls snmpwalk -v 2c -c<RW_CommString> -m all -M <MIB-DIR> udp6:<IPv6>MOCA20-MIB::mocaMeshScMod HEX to IntegerMOCA20-MIB::mocaMeshScMod.1.0.1.0 = Hex-STRING: 00 00 00 00 08 08 09 0A0A 0A 0A 0A 0A 0A 0A 0A0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 09 09 09 0909 09 09 09 09 09 09 09 09 09 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A0A 0A 0A 0A 0A 0A 0A 0A 0A 09 09 09 09 09 09 09 09 09 09 09 09 0A 0A 0A0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A0A 0A 0A 0A 0A 0A 0A 09 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 09 09 0A09 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 09 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A0A 0A 09 09 09 09 09 0A 09 09 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 00 00 00 0000 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 07 07 0708 08 07 07 07 07 06 06 06 05 06 06 06 07 07 08 08 08 09 09 09 09 09 0909 09 09 09 09 09 09 09 09 09 09 09 09 09 09 09 09 08 08 08 07 07 07 0707 07 08 08 09 09 09 09 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A0A 0A 0A 0A 09 09 09 09 08 08 07 07 06 07 07 07 08 09 09 09 0A 09 0A 0A0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 09 09 09 09 08 0808 08 08 08 09 09 09 09 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A0A 0A 0A 0A 0A 0A 0A 0A 09 09 09 09 09 08 08 08 09 09 09 09 09 0A 0A 0A0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 09 0A09 09 09 09 09 09 09 09 09 09 09 09 0A 09 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 0A 09 09 09 09 09 08 08 08 08 07 00 00 00

In gathering data from a device via WebPa, all of the devices on theMoCA network may support the custom parameters of Table 6 and thebehavior of said parameters may match the implementation of their SNMPcounter-parts.

TABLE 6 Name Type Write DescriptionDevice.MoCA.Interface.{i}.X_MOCALLIANCE_ORG_Aca. object —[mocaIfAcaTable/1.3.6.1.4.1.31621.1.2. 1.5] This diagnostic test maycorrespond to the MoCA ACA operation. When this test is requested, themanaged MoCA Node may initiate a MoCA On Demand Link MaintenanceOperation (LMO). NodeID unsignedInt- W[mocaIfAcaNodeID/1.3.6.1.4.1.31621.1. [0:15] 2.1.5.1.1] [MocaNodeID] TheNode ID of the MoCA Node may transmit the EVM Probe when the parametertype = evm. Type string W [mocaIfAcaType/1.3.6.1.4.1.31621.1.2. 1.5.1.2]The ACA type may be either evm or quiet. Enumeration of: quiet evmChannel unsignedInt W [mocaIfAcaChannel/1.3.6.1.4.1.31621. 1.2.1.5.1.3]The channel number under assessment, may start from 0 and increase inincrements of 25 MHz. ReportNodeMask hexBinary(2:2) W[mocaIfAcaReportNodeMask/1.3.6.1.4. 1.31621.1.2.1.5.1.4] Specifies theMoCA Nodes that are requested may be part of the channel assessment: mayset bits corresponding to Node IDs of these MoCA Nodes to 1 (LSB maycorrespond to Node ID 0x0). DiagnosticsState string W May indicate theavailability of diagnostics data. Enumeration of: None (READONLY)Requested (ACA operation requested and in progress) Canceled (OPTIONAL)Complete (ACA operation completed successfully, READONLY) Error (ACAoperation failed, READONLY) Error_BadChannel (ACA operation failedbecause of bad channel, READONLY) Error_NoEvmProbe (ACA operation failedbecause there was no Evm probe detected, READONLY) If the ACS sets thevalue of this parameter to requested, the CPE may initiate thecorresponding diagnostic test. When writing, the values requested andcanceled may be allowed. To ensure the use of the proper test parameters(the writable parameters in this object), the test parameters may beset, and any errors or inconsistencies in the test parameters may bedetected, prior to or at the same time as (in the sameSetParameterValues) setting this parameter to requested. When requested,the CPE may wait until after completion of the communication sessionwith the ACS before starting the diagnostic test. When the test iscompleted, the value of this parameter may be Complete (if the testcompleted successfully), or one of the Error values listed above. If thevalue of this parameter is not Complete, the values of the resultsparameters for this test may be indeterminate. When the diagnosticinitiated by the ACS is completed (successfully or not, but notcanceled), the CPE may establish a new connection to the ACS to allowthe ACS to view the results, which may indicate the Event code 8DIAGNOSTICS COMPLETE in the Inform message. After the diagnostic iscomplete, the values of all resulting parameters (e.g., all read-onlyparameters in this object) may be retained by the CPE until either thisdiagnostic is run again, or the CPE reboots. After a reboot, if the CPEhas not retained the result parameters from the most recent test, it mayset the value of this parameter to none. Modifying any of the writableparameters in this object (except for this one) may result in the valueof this parameter being set to none. While the test is in progress,modifying any of the writable parameters in this object except for thisone may result in the test being terminated and the value of thisparameter being set to none. While the test is in progress, setting thisparameter to requested (and possibly modifying other writable parametersin this object) may result in the test being terminated and thenrestarted using the current values of the test parameters. While thetest is in progress, setting this parameter to canceled may result inthe test being canceled and the value of this parameter being set tonone. If the CPE does not support the canceled value, it may return aSPV error with “Invalid Parameter value” (9007) to the ACS instead.

The ACA may be obtained via WebPa (or TR-069 custom namespace) using theCLI procedure described above (e.g., substituting WebPa for SNMP).

The ACA error vector magnitude (EVM) and QUIET probe data may beretrieved as described below. In a mesh environment, the user may applythe ACA EVM probe to each of the MoCA devices. The ACA probe maycomprise 512 data points. Each data point may represent an OFDMsubcarrier receive power in dBm. The first active subcarrier may beidentified as the MoCA channel. For example, for the MoCA channel D1(1150 MHz), the first active subcarrier may be 1150 MHz

Users may ignore the reverse EVM probe path. The performance manager 312may perform a reverse ACA EVM probe for detecting path loss asymmetry.Path loss asymmetry may be a condition associated with older generationnon-MoCA-friendly drop amplifiers during VoIP/passive port connections.

FIG. 33 shows results of a bi-directional EVM probe. The additional EVMprobe in the opposite direction may not be required. A bi-directionalACA may be warranted. The bi-directional PHY rate may be within 10-20Mbps apart. As described herein, MoCA Mesh PHY Rate may be a keyperformance indicator (KPI). In of Table 7, Node1 (Transmitter(Tx))|Node2 (Receiver (Rx)) may be 149 Mbps, whereas the reverse may be220 Mbps. Where the discrepancy is greater than 50 Mbps, abi-directional ACA may be warranted to verify that there is not an RFimpairment.

TABLE 7 Full Mesh Rate (FMR) Node 1 2 1 — 220 Mbps 2 149 Mbps —

An EVM Probe feature may be analogous to a Network Analyzer measuringthe per-sub-carrier magnitude receive power in dBm on a per MoCA Channelbasis. For MoCA 2.0, the MoCA channel bandwidth may be 100 MHz. An EVMprobe may be a BPSK modulated signal using 32 different symbols. A QuiteProbe may silence the MoCA network to measure any system noise withinthe MoCA channel selected.

Results of an EVM probe may be normalized. It may be required to performa correction of the EVM probe data for a better representation of theactual OP2OP isolation,. In this example, the system on a chip (SoC)implementation conducts an AGC of the receive EVM probe. When graphingthe power levels over frequency (FIG. 24), it may appear to be a moreflat response, with possible oscillation, or ripples across the OFDM.This may not correspond with what is happening on the wire. A correctionof the EVM probe data may be performed to verify the actual response.For example, the EVM probe data may be normalized as discussed withreference to FIGS. 24-25 and equations 2-5. Normalization may utilizemocalfAcaTotalRxPower and mocalfAcaPowerProfile. MocalfAcaTotalRxPowermay correspond to the actual measured 100 MHz receive power in dBm,including OFDM guard bands at the F-Connector, whilemocalfAcaPowerProfile may correspond to the per-subcarrier processedreceive EVM in dBm.

Observing the slope of the tilt may indicate the impedance reactanceover frequency and/or the Node to Node spectral tilt frequency response.A calculated slope ratio close to 1:1 may point to a flat response. Thepercentage of the variable may indicate the splitter Port-to-Portperformance over frequency. Simple Regression analysis may be used tocheck for tilt. An EVM ACA response may contain a total of 512 datapoints, which may include the OFDM guard bands. The Simple Regressioncalculation may take into account all 512 points. While determining theslope or tilt, the guard band subcarriers may be removed from the listof elements for a more accurate result. The sub carrier indexes may beas shown in Table 8.

TABLE 8 MoCA 2.0 Guard Band Sub Carriers MoCA 2.0 Guard Band Subcarrier(32 SC) Lower Edge 0, 1, 2, 3 Center Sub Carriers 244, 245, 246, 247,248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261,262, 263, 264, 265, 266, 267, 268 Upper Edge Sub Carriers 509, 510, 511

Below is a method for determining tilt slope in pseudo code that may beimplemented using a programming language such as Java:

TABLE 9 Example Tilt Slope Pseudo Code publicSimpleRegressionCalculation { private SimpleRegression sr; publicSimpleRegressionCalculation (List<Double> ldAcaEvmQuietData) {   this.sr = new SimpleRegression(ldAcaEvmQuietData); } publicList<Double> getTrendOrTilt( ) {    ArrayList<Double> ldTrend = newArrayList<Double>( );    double dSlope = this.sr.getSlope( );    doubledInter = this.sr.getIntercept( );    /* f(x) = (Slope * x) + Intercept*/    double x = 0;    for (Double d : app.getAcaPowerProfile(false)) {    ldTrend.add(((x++)*dSlope)+dInter);    }    return ldTrend;    } }

Slope delta may be determined by Equation 6:

Tilt₂[n−1]−Tilt₁ _([0]) =SlopeDelta

where n=ACA[x].length

SlopeDelta>0=Positive Tilt

SlopeDelta<0=Negative Tilt  Equation 6 Slope Delta

The Slope Delta may be an indication of the severity of the frequencyresponse of the ACA over 100 MHz. FIG. 34 shows a slope delta wheren=512. FIG. 34 may indication a 120 ns Delay Signal with Positive Tiltof 5.7 dB.

Inside a coaxial home network, and depending on the complexity of thesplitter network, all transmitted signal may be subject to signalreflections interference. The delay of the signal and its overallcomposite power may contribute to the signal reflections interference.The composite power may be reduced by having a high return loss (RL) atthe F-connector. The RL value may be the reduced level of the signalpower in dB that the reflected signal may inject back into the coaxnetwork.

Ripples and Nulls in the ACA data may be the results of the reflectedsignal interacting with (e.g., canceling out) the original signal. Thehigher the composite power of the reflected signal, the deeper thenulls, or the higher the “Peak to Valley” ratio of the ripple. Nulls mayappear on the left side and ripples may appear on the right.

In the shown example of FIG. 35, there may be twelve nulls or valleys inthe ACA response. The signal delay and/or distance of the POE from theinput of the root splitter may be approximated based on Equation 7.

$\begin{matrix}{\mspace{20mu} {{{{Calculate}\mspace{14mu} {Delay}\mspace{14mu} {Signal}\mspace{14mu} {in}\mspace{14mu} {{Time}\left\lbrack \frac{{OFDM}\mspace{14mu} {Occupied}\mspace{14mu} {Bandwith}}{{Number}\mspace{14mu} {of}\mspace{14mu} \left( {Nulls} \middle| {Peak} \middle| {Valley} \right)} \right\rbrack}^{- 1}} = {{{Delay}\mspace{14mu} {Signal}\mspace{14mu} {in}\mspace{14mu} {{time}\mspace{20mu}\left\lbrack \frac{100\mspace{14mu} {MHz}}{12({Null})} \right\rbrack}^{- 1}} = {{120\mspace{14mu} {ns}} = {120\mspace{14mu} {feet}}}}}\mspace{20mu} {{RG}\; 6\mspace{14mu} {Cable}\mspace{14mu} {Speed}\mspace{14mu} {of}\mspace{14mu} {\left. {Light} \right.\sim\frac{1\mspace{14mu} {ns}}{feet}}}{\frac{120\mspace{14mu} {ns}}{2} = {60\mspace{14mu} {Feet}\mspace{14mu} {assuming}\mspace{14mu} 120\mspace{14mu} {ns}\mspace{14mu} {of}\mspace{14mu} {round}\mspace{14mu} {trip}\mspace{14mu} {delay}}}}} & {{Equation}\mspace{14mu} 7}\end{matrix}$

Reflections may be detected via Line Cross-Section Method. ACA examplesfrom strong to minimum reflections may be caused by a POE filter and/orconfiguration of the coax network. Once the ACA is collected from a MoCAreporting node, a grooming or normalization process may be utilized toprepare the raw ACA data for analysis.

A MoCA 2.0 OFDM Channel may comprise of two OFDM blocks. FIG. 36 showseach (left/right) OFDM block at 50 MHz. A first step may be to separatethe two OFDM blocks, and remove the subcarrier guard bands that aredefined in Table 8. The resulting data may include the EVM probemagnitude points as depicted in FIGS. 39-42.

As shown in FIG. 36, the ACA results may reflect high-frequency spikesand nulls riding on the sinusoidal edge of the OFDM sidebands. Theperformance manager 312 may utilize a cross-section line technique tocount the number of spikes/nulls and/or the number of crosses from aspike to a null. A high cross-section count may be caused by thehigh-frequency spikes and nulls. FIG. 37 shows a 3-point moving averageexample, while FIG. 38 shows 10-point moving average example. Theperformance manager 312 may utilized a Window Moving Average (WMA)signal process technique, which may act as a low-pass filter and mayreject high-frequency noise. Equation 8 shows a sliding window movingaverage equation. The sliding window algorithm may have a runtimecomplexity of O (n* ws), where ws=Window Size.

$\begin{matrix}{\mspace{20mu} {{{{Sliding}\mspace{14mu} {Window}\mspace{14mu} {Moving}\mspace{14mu} {Average}}\begin{matrix}{x\lbrack 0\rbrack} & {x\lbrack 1\rbrack} & {x\lbrack 2\rbrack} & {x\lbrack 3\rbrack} & {x\lbrack 4\rbrack} & {x\lbrack 5\rbrack} & {x\lbrack 6\rbrack} & {x\lbrack 7\rbrack} & {x\lbrack 8\rbrack} & {x\lbrack 9\rbrack}\end{matrix}\mspace{20mu} {{{x\lbrack k\rbrack} = {\sum\limits_{k = 0}^{n}\frac{{x\lbrack k\rbrack} + {x\left\lbrack {k + \left( {n - 1} \right)} \right\rbrack} + \ldots}{n}}},\ldots}\mspace{20mu} {{k = {{arrar}\mspace{14mu} {index}}},{x = {{ACA}\mspace{14mu} {array}}},{n = {{window}\mspace{14mu} {size}}}}}\mspace{20mu} {{{x\lbrack 0\rbrack} = \frac{{x\lbrack 0\rbrack} + {x\lbrack 1\rbrack} + {x\lbrack 2\rbrack}}{3}},\mspace{20mu} {{x\lbrack 1\rbrack} = \frac{{x\lbrack 1\rbrack} + {x\lbrack 2\rbrack} + {x\lbrack 3\rbrack}}{3}},\ldots}}} & {{Equation}\mspace{14mu} 8}\end{matrix}$

Below is a method for determining sliding moving average in pseudo code:

TABLE 10 Example Sliding Moving Average Pseudo Code public classMovingAverage {    private List<Double> ldEntry = new ArrayList<Double>();    public MovingAverage(List<Double> ldData) {    this.ldEntry.addAll(ldData);    }    public List<Double>getAverage(int iWindow) {     List<Double> ldAverage = newArrayList<Double>( );     double dSum = 0;     double dAverage = 0;    int iWindowDivisor = 0;     /* Traverse thru Data Set */     for(int iIndex = 0; iIndex < ldEntry.size( ); iIndex++) {       /* ResetAverage/Sum */       dSum = 0;       dAverage = 0;       iWindowDivisor= 0;       for (int iWindowIndex = iIndex, iWindowIndexCount = 0;          iWindowIndexCount < iWindow;    iWindowIndex++,iWindowIndexCount++) {         if (iWindowIndex >= ldEntry.size( )) {          break;         }         /* Update Sum */         dSum +=ldEntry.get(iWindowIndex);     /*     This is require so to adjust thedenominator value when the     Index reaches the end of the list size    */         ++iWindowDivisor;       }       /* Moving Average applySliding Window Number */       dAverage = dSum / (double)iWindowDivisor;       ldAverage.add(dAverage);     }     returnldAverage;    } }

Using the dissected OFDM processed data described herein, theperformance manager 312 may utilize Descriptive Statistics (DStat)methods to estimate the correct Window Size (WS). A large WS (e.g., FIG.38) may average a signal to the point that it smooths the ripples ornulls and may not be detected. A small WS may not smooth the signalenough and may cause errors in cross-section counting (e.g.,peak-to-valley ripple or null being<3 dB).

A number of ripple peak-to-peak or nulls may be estimated to determinethe signal delay and/or estimate the reflection point caused by the POE.Adequate reflected signal power and signal delay may be required tocreate the ripple and nulls. If the POE is too close to the input of asplitter (e.g., less than ten feet) or when there are only two MoCAnodes on the network with a unity gain amplifier (UGA), there may be nomeasurable ripple or nulls.

The Cross-Section technique may similar to counting Zero-Crossing with apure AC signal and removing the DC component. The ACA EVM data may be nodifferent, but due to the return loss at different frequencies, it maybe problematic during conversion. The following techniques may be usedwithout having to convert to a pure AC signal.

The Mean Cross-Section technique may be a simple average of the sum ofthe points divided by the number of points as set forth by Equation 9.

$\begin{matrix}{{{Calculating}\mspace{14mu} {Mean}\mspace{14mu} {from}\mspace{14mu} {ACA}\mspace{14mu} {EVM}\mspace{14mu} {Sideband}\mspace{14mu} {without}\mspace{14mu} {Subcarrier}\mspace{14mu} {Guard}\mspace{14mu} {Band}}\mspace{20mu} {{EVM}_{Mean} = {\frac{1}{n}{\sum\limits_{i = 0}^{n}{{EVM}_{{OFDM}\mspace{14mu} {SideBand}}\lbrack i\rbrack}}}}} & {{Equation}\mspace{14mu} 9}\end{matrix}$

FIG. 40 shows a calculated Mean of −29.6 dB. To determine a crossing,the EVM_(OFDM) sideband array may be traversed and compared against themean value. The number of times the mean is crossed may be counted,which provides the cross-section count. If the cross-section count is anodd number, a modulus operation check may be performed for a zeroremainder. If a reminder is calculated, one may be added to the count.

Below is a method for determining mean crossing count in pseudo codethat may be implemented using a programming language such as Java:

TABLE 11 Example Mean Crossing Count (Pseudo Code) public intmeanCrossingCount(OfdmBlock.LEFT_BLOCK) { /* getSingleSideOfdmDescStat() perfoms a 10-Point Moving Average */   DescriptiveStatistics        ds         =getSingleSideOfdmDescStat(OfdmBlock.LEFT_BLOCK);    double dMean =ds.getMean( );    List<Double> ldDS = ds.getValues( );    intiCrossedMean = 0;    /* True = Above ; False = Below */    booleanbnCrossMeanUpDown = false;    if (ldDS.get(0) < dMean) {    bnCrossMeanUpDown = false;    } else {     bnCrossMeanUpDown = true;   }    for (Double d : ldDS) {     if (d < dMean) {       if(bnCrossMeanUpDown == true){         iCrossedMean++;        bnCrossMeanUpDown = false;         Print(“Below Mean (“ +iCrossedMean + ”)Times”);       }     } else if (d > dMean) {       if(bnCrossMeanUpDown == false){         iCrossedMean++;        bnCrossMeanUpDown = true;         Print(“Above Mean (“ +iCrossedMean + ”)Times”);       }     }    }    If (iCrossedMean%2 != 0){ iCrossedMean++;}    return iCrossedMean; }

FIG. 43 Left OFDM and FIG. 44 Right OFDM illustrate different levels oftilt. FIG. 43 shows a more flat tilt whereas FIG. 44 shows a tilt with apositive slope. As shown in FIG. 44, there are five cross-line points,each of which the Mean Cross-Section test may have missed. TheTilt/Trend Cross-Section technique may prevent overlooking of a linecrossing.

Using the Tilt Slope, the performance manager 312 may use the ACA EVMdata without the subcarrier guard bands to get a more accuratetrend/tilt cross-section line.

Below is a method for determining trend or tilt crossing count in pseudocode that may be implemented using a programming language such as Java:

TABLE 12 Example Trend or Tilt Crossing Count - PSEUDO CODE public inttiltCrossingCount(OfdmBlock.RIGHT_BLOCK) {  /* This list contains allthe Y Intercepts of the Trent/Tilt Line as a f(x) */ List<Double>        ldTrend            =getTrend(getMovingAverageNoGuardBandOFDM (OfdmBlock.RIGHT_BLOCK));  intiCrossedTilt = 0;  /* True = Above; False = Below */  booleanbnCrossTiltUpDown = false;  if (ldDS.get(0) < ldTrend.get(0)) {    bnCrossTiltUpDown = false;  } else {     bnCrossTiltUpDown = true; }  int iTrendIdx = 0;  for (Double d : ldDS) {     if (d <ldTrend.get(iTrendIdx)) {      if (bnCrossTiltUpDown == true) {       iCrossedTilt ++;        bnCrossTiltUpDown = false;       Print(“Below Tilt (“ + iCrossedTilt + ”)Times”);      }     }else if (d> ldTrend.get(iTrendIdx)) {      if (bnCrossTiltUpDown ==false) {        iCrossedTilt ++;        bnCrossTiltUpDown = true;       Print(“Above Tilt (“ + iCrossedTilt + ”)Times”);      }     }    iTrendIdx++;  }     If (iCrossedTilt %2 != 0) { iCrossedTilt ++;}    return iCrossedTilt; }

In FIG. 44, the performance manager 312, using the Mean CrossingDetection, may run the risk of missing two intersection points at thethird peak and the last valley. Variances in amplitude at differentfrequencies may be tracked when using the tilt or trend method.

The Receive Channel Power (RCP) may be the measured energy at theF-connector of a MoCA device. Depending on the type of ACA test EVMQUIET, the RCP may report its value in dBm.

Equation 10 set forth an OFDM Occupied BW Channel Power Calculation:

$\begin{matrix}{\mspace{20mu} {{{Channel}\mspace{14mu} {Power}\mspace{14mu} {Calculation}}{{EVMBin} = \left. {{The}\mspace{14mu} {stored}\mspace{14mu} {array}\mspace{14mu} {of}\mspace{14mu} {EVM}} \middle| {{QUIET}\mspace{14mu} {Probe}\mspace{14mu} {dBm}\mspace{14mu} {levels}\mspace{14mu} {from}\mspace{14mu} {index}\mspace{14mu} {SubCarrer}_{0}\mspace{14mu} {to}\mspace{14mu} {index}\mspace{14mu} {SubCarrier}_{N - 1}} \right.}{{{EVM}_{CPCalc}\mspace{14mu} {in}\mspace{14mu} {dBM}} = {{The}\mspace{14mu} {Total}\mspace{14mu} {Channel}\mspace{14mu} {Power}\mspace{14mu} {calculated}\mspace{14mu} {from}\mspace{14mu} {the}\mspace{14mu} {EVM}\mspace{14mu} {Probe}\mspace{14mu} {Data}}}{{{EVM}_{CPCalc}{dBm}} = {10*{\log_{10}\left( {\overset{{{EVMBin}.{size}} - 1}{\sum\limits_{n = 0}}\left\lbrack 10^{\frac{{EVMBin}{(n)}}{10}} \right\rbrack} \right)}}}}} & {{Equation}\mspace{14mu} 10}\end{matrix}$

The per-sub-carrier bit loading value may be the digital representationof the MoCA OFDM channel. These stats may comprise 480 of 512 activesubcarriers (Table 8). The bit load may represent the number ofbits-per-symbol occupied within a subcarrier. In MoCA, a subcarrier BWmay be 195.3125 kHz. The symbol rate may be ˜195.3125 kbpsym (regardlessof the number of bits). Based on this, a baud rate may be calculated forthe specific subcarrier according to Equation 11. Equation 11 may nottake into account the cyclic-prefix (CP), FFT roll-off period(Windowing), or other time variant dependencies that may be built intothe OFDM demodulation process.

BitsPerSymbol*(˜195.3125*10³)=SubCarrier BaudRate  Equation 11SubCarrier BaudRate

FIG. 45 and FIG. 46 illustrate example reported MoCA OFDM per subcarrierbit load. This example is the bit load representation of the ACA EVMprobe in FIG. 38.

PHY Rate and Bit Load Avg may be determined by Equations 12 and 13 andmay be used to determine a strong likelihood of MoCA Home Readiness.

$\begin{matrix}{{{Calculate}\mspace{14mu} {PHY}\mspace{14mu} {Rate}\mspace{14mu} {from}\mspace{14mu} {MeshScMod}\mspace{14mu} {Bit}\mspace{14mu} {load}\mspace{14mu} {Stat}}{{{Calculated}\mspace{14mu} {Phy}\mspace{14mu} {Rate}} = {\left\lbrack {\sum\limits_{i = 0}{{Bitload}\lbrack i\rbrack}} \right\rbrack*195.3125*10^{3}}}} & {{Equation}\mspace{14mu} 12} \\{{{Calculate}\mspace{14mu} {Bit}\mspace{14mu} {load}\mspace{14mu} {Avg}\mspace{14mu} {from}\mspace{14mu} {MesScMod}\mspace{14mu} {Bit}\mspace{14mu} {load}\mspace{14mu} {Stat}}\mspace{20mu} {{Bitload}_{Avg} = {\frac{1}{n}{\sum\limits_{i = 0}^{n = 480}{{Bitload}\lbrack i\rbrack}}}}} & {{Equation}\mspace{14mu} 13}\end{matrix}$

The maximum distance of a POE from one MoCA node to another may becalculated, within one ACA operation. The POE filter may not be directlyconnected to the input of the splitter, the POE may be at least 10 feetfrom the root splitter and there may not be UGA installed in the homecoax network. The performance manager 312 may determine a delay signalproduced by the POE, based on its near perfect 0 dB Return Loss withinthe MoCA D-Channel frequency band.

The Recursive Line-Crossing Symmetry Check (RLCSC) may be a two-stepprocess. Using the Trend Cross-Section method described herein to findthe indexes that cross the trend/tilt line, the performance manager 312may calculate the differences between pairs.

The Arithmetic Sliding Window (ASW) may be used to calculate thedistance between the crossed intersections of the ripple waveform. Belowis a method for determining arithmetic sliding window in pseudo code.

TABLE 13 Example Arithmetic Sliding Window Pseudo Code /**  *   SlidingWindow of 2 - Add|Sub (Index[0] and Index[1])  *   ([0],[1]),([2],[3]) *  * @param li List<Integer> List of Trend|Tilt Crossing Indexes  *@param iWindowSize Set to 2 for Two Index Calculation  * @param bnAddSubTrue = Add , False = Subtract  Set to FALSE  * @return List<Integer> =Calculated Distances  */ public List<Integer>arithmeticSlidingWindow(List<Integer> li, int iWindowSize , booleanbnAddSub) {      List<Integer> liSlideWindowResult = newArrayList<Integer>( );      Integer iMathResult = 0;      for (int iIdx= 0; iIdx < (li.size( )−1); iIdx++) {       /* Reset Result for nextIndex */       iMathResult = 0;   /* *******************SetBoundary*************************/       for  (int  iWindowSlider  =  0;(iWindowSlider < iWindowSize)&&((iIdx+iWindowSlider) < li.size( ));iWindowSlider++) {         if (bnAddSub) {           /* Adding Slider */          iMathResult += li.get(iIdx+iWindowSlider);         } else {          /* Subtracting Slider */          iMathResult  =  Math.abs(iMathResult)  −li.get(iIdx+iWindowSlider);         }       }      liSlideWindowResult.add(Math.abs(iMathResult));      }      returnliSlideWindowResult;  }

The performance manager 312 may determine standard deviation. The spreadof the distances in a symmetrical ripple or nulls may be close to zerospread (e.g., a spread of zero may be unrealistic in the real world).The performance manager 312 may estimate the ripple Peak-To-Peak orNull-To-Null with little computation complexity.

Below is a method for determining Computation Complexity: O (n) inpseudo code.

TABLE 14 Example Recursive Line-Crossing Symmetry Check Pseudo Codeprivate void processUpperLowerTiltCrossings( ) {    this.ldDS =convertArrayToList(ds.getValues( ));    this.ldTrend = getTrend   (SignalProcess.getSimpleRegressionFromOFDM(ldDS));   this.iCrossedTilt = 0;    /* True = Above ; False = Below */   boolean bnCrossTiltUpDown = false;    boolean bnCrossTiltUpDownStart= false;    if (ldDS.get(0) < ldTrend.get(0)) {     bnCrossTiltUpDown =false;    } else {     bnCrossTiltUpDown = true;    }   bnCrossTiltUpDownStart = bnCrossTiltUpDown;    if (bnCrossTiltUpDown){     logger.debug(“Trend-Tracking-Start-Above Trend-Line @ (“ +ldDS.get(0) + ”)”);    } else {    logger.debug(“Trend-Tracking-Start-Below Trend-Line @ (“ +ldDS.get(0) + ”)”);    }    int iTrendIdx = 0;    for (Double d : ldDS){     if (d < ldTrend.get(iTrendIdx)) {       if (bnCrossTiltUpDown ==true){         iCrossedTilt++;         bnCrossTiltUpDown = false;        this.liTiltCrossingIndexTallyLower.add(iTrendIdx);    /* CrossedInto Lower */        this.liTiltCrossingIndexTallyUpper.add(iTrendIdx−1); /* JustLeft Upper */         logger.debug(“Below Tilt (“ + iCrossedTilt + ”)Times @ Index: (“ + iTrendIdx + ”)”);       }     } else if (d >ldTrend.get(iTrendIdx)) {       if (bnCrossTiltUpDown == false){        iCrossedTilt++;         bnCrossTiltUpDown = true;        this.liTiltCrossingIndexTallyUpper.add(iTrendIdx);        this.liTiltCrossingIndexTallyLower.add(iTrendIdx−1);        logger.debug(“Above Tilt (“ + iCrossedTilt + ”) Times @ Index:(“ + iTrendIdx + ”)”);       }     }     iTrendIdx++;    }    if(bnCrossTiltUpDownStart) {    this.liTiltCrossingIndexTallyUpper.remove(0);    } else {    this.liTiltCrossingIndexTallyLower.remove(0);    }    if(bnCrossTiltUpDown) {    this.liTiltCrossingIndexTallyUpper.remove(this.liTiltCrossingIndexTallyUpper.size ( )−1);    } else {   this.liTiltCrossingIndexTallyLower.remove(this.liTiltCrossingIndexTallyLower.size ( )−1);    } }

FIG. 47 shows an algorithm for Recursive Line-Crossing Check. ThePeak_(Mean) may be selected such that Equation 13 is satisfied.

0.5[([2*16.25_(PeakMean])*195.3125×10³)⁻¹]=78.77 ns  Equation 13Recursive Line-Crossing Symmetry Check Signal Delay Calculation

The distance of the POE or delay within an accuracy of 10 feet or 10 nsmay be estimated by counting the number of tilt line crossing over thebandwidth (e.g., the total delay in nanoseconds (ns)). One cycle or twotilt crossings within an OFDM block may equate to 10 ns or 10 feet. Ifthe number of crossing is odd, the performance manager 312 may add oneto the tally of crossings to make the count even.

FIGS. 48A-48B illustrate a Tilt Crossing Counting with a POE at 80 Feetwith a 2Way Splitter. Equation 14 sets forth an equation for determiningdelay signal using tilt crossing:

$\begin{matrix}{\mspace{20mu} {{{Delay}\mspace{14mu} {Signal}\mspace{14mu} {Calculation}\mspace{14mu} {Using}\mspace{14mu} {Tilt}\mspace{14mu} {Crossing}}{\frac{{.5}\left( {{{Number}\mspace{14mu} {of}\mspace{14mu} {Tilt}\mspace{14mu} {Crossing}} + \left\lbrack {{Add}\mspace{14mu} 1\mspace{14mu} {if}\mspace{14mu} {odd}} \right\rbrack} \right)}{50\mspace{14mu} {MHz}_{Sideband}} = {Delay}_{n\; s}}}} & {{Equation}\mspace{14mu} 14}\end{matrix}$

A 3 to 10 Point Window Moving Average may be required to smooth thewaveform as discussed above.

The performance manager 312 may utilize the Tilt of Trend Cross-Sectionalgorithm described herein to determine tilt cross section.

A Fourier Transform (FT) may be performed to decompose a function oftime (e.g., in the time domain) into discrete frequencies (e.g., in thefrequency domain). Although the probe data (e.g., ACA data) may be afrequency domain representation, an FFT (or Fast Fourier Transform(FFT)) may still be performed similarly to the Recursive Line Crossingalgorithm to acquire a frequency response and determine the frequency ofa ripple.

A Discrete Fourier Transform (DFT) may have a higher computationalcomplexity than the FFT, but may also provide a frequency response.Equation 15 is an equation for performing a DFT.

$\begin{matrix}{{{Discrete}\mspace{14mu} {Fourier}\mspace{14mu} {Transform}\mspace{14mu} ({DFT})}{{X(k)} = {\sum\limits_{t = 0}^{n - 1}{{x(t)}e^{{- 2}\pi \; {{tk}/n}}}}}} & {{Equation}\mspace{14mu} 15}\end{matrix}$

The probe data (e.g., ACA data) may be normalized before FFT Analysis.For example, ACA probe data may have a sample size of 512 Log-Magnitudepoints. The FFT points may be the number of sample points that may besubmitted to the FFT. The total number of points may be a power of 2.For a better resolution, zero-padding may be done to extend the numberof FFT points, as shown by Equation 16.

FFT[i]_(Points(1024)) ={X _([0]) =X _(ACA) _(dBm) ,X _([1]) =X _(ACA)_(dBm) , . . . , X _([512]) =X _(ACA) _(dBm) ,X _([512])=0_(ZeroPad) , .. . ,X _([1023])=0_(ZeroPad)}  Equation 16 FFT Point Zero-PaddingTechnique

The ratio between sample points and FFT points may be less than or equalto [1:2 or 0.5]. For example, the ratio between sample points and FFTpoints may be [1:8 or 0.125]. Equation 17 sets forth determination ofthe ratio:

$\begin{matrix}{{{FFT}\mspace{14mu} {Scale}\mspace{14mu} {Calculation}}{{FFT}_{Scale} = {\frac{{ACA}_{SampleSize}}{{FFT}_{Points}} = {{1\text{:}8} = {.125}}}}} & {{Equation}\mspace{14mu} 17}\end{matrix}$

For signal delay detection, 512 zero's may be added to the end of theACA probe data list of values, for a total of 1024.

Before submitting the 1024 sample points, each ACA probe data value mayrequire conversion from dBm to a linear scale. Equations 18 and 19illustrate such a conversion:

$\begin{matrix}{{{dBm}\mspace{14mu} {to}\mspace{14mu} {Linear}}x_{Linear} = 10^{\frac{x_{dBm}}{10}}} & {{Equation}\mspace{14mu} 18} \\{{{Linear}\mspace{14mu} {to}\mspace{14mu} {dBm}}{x_{dBM} = {10*{{Log}\left( x_{Linear} \right)}}}} & {{Equation}\mspace{14mu} 19}\end{matrix}$

The FFT results may be calculated to obtain a ripple signal found in theACA probe data. After executing the FFT, the a list of complex numbersmay be produced. To calculate the magnitude power of the givenIndex_(Freq) in hertz (Hz), the magnitude power for each complex numberindex may be calculated according to Equation 20.

Magnitude Power_(Linear)=√{square root over(Real²+Imaginary²)}  Equation 20 Complex Number Magnitude PowerCalculation

When graphing, the linear magnitude power may be converted to dBm(Equation 19) or dB (Equation 21) to have a more clear FFT frequencyresponse.

dB=Log(MagnitudePower_(Linear))  Equation 21 Linear to dB Calculation

Because the FFT may convert a time-domain signal to a frequency-domainresponse, the ACA probe data may look like a square wave from atime-domain signal POV. For example, the ACA probe data may comprise twoOFDM channels, each comprising 256 points for a total of 512 points.After the FFT, the ripple may be identified, by determining the indexlocation, in some examples, before it is scaled. The scaling process maydetermine the actual frequency of the ripple.

The ripple in the ACA probe data may be determined using FFT. FIGS.49A-49C illustrate an FFT of a first node (e.g., a POE filter) at 75feet from a second node (e.g., the input of a root splitter). As statedearlier, the FFT may be used to determine the ripple frequency acrossthe one MoCA 100 MHz channel. The minimum delay that may be analyzed maybe 20 ns.

FIG. 49A shows an EVM probe data response of a POE filter 75 feet fromthe input of a 3WAY MFS where no moving average was applied oradditional interpolation to increase the sample size. The FFT points maybe 4096 in a sample size of 512. FIG. 49B shows an EVM FFT frequencyresponse of 2046 FFT Points. Using the FFT Scale Calculation of Equation17, the an FFT_(Scale) of 0.125 may be determined. FIG. 49B shows twomajor peaks in the lower frequencies.

FIG. 49C shows the first 184 FFT points (e.g., 1 Hz to 184 Hz). Theshaded region may represent the frequencies that make up the part of thesquare-wave EVM response. With a MoCA channel BW of 100 MHz, any delayof 20 ns or less may not render a ripple on the OFDM carriers. In 49C,the threshold region may be from 1 Hz to 16 Hz.

The threshold region corresponding to a minimum delay of 20 ns may bedetermined according to Equation 22:

$\begin{matrix}{\mspace{20mu} {{{20\mspace{14mu} {ns}\mspace{14mu} {index}\mspace{14mu} {Frequency}\mspace{14mu} {Threshold}}\mspace{20mu} {{Index}_{{FrequencyThreshold}{({FT})}} = \frac{20*10^{- 9}}{{FFT}_{Scale}\left( \left\lbrack T_{s} \right\rbrack^{- 1} \right)}}T_{s}} = {{SampleRate} = {{NumberOfMoCAChannels}\left( {100*10^{6}} \right)}}}} & {{Equation}\mspace{14mu} 22}\end{matrix}$

Excluding the threshold region, the Highest Magnitude Peak (HMP) may bedetermined as depicted in FIG. 49C and Equation 23 may be executed toobtain the round trip signal delay at the frequency where the rippleoccurs.

SignalDelay_(RoundTrip)=[FFT_([HMP])*FFT_(Scale)]*[T _(s)]⁻¹  Equation23 Echo Round Trip Delay from FFT Response

Because signal propagation may not be perfect through electricalconnections (e.g., not the speed of light), a delay factor may bedetermined based on the type of connections used in the premisesnetwork. The actual propagation delay, assuming an RG6 cable is used ina home, may be determined in accordance with Equation 24. Not allcoaxial cable have the same Nominal Velocity of Propagation (NVP). Thepercentage coefficient may be multiplied to normalize the signal delayresults.

SpeedOfLight=1.016703362E⁻⁹

RG6_(NVP)=84%=0.84

SignalDelay_(RG6(NVPCorrection))=[SpeedOfLight*RG6_(NVP)]=0.854030824  Equation24 RG6 Signal Delay Correction Coefficient

SignalDelay_(RoundTrip(RT))=SignalDelay_(RG6(NVPCorrection))*SignalDelay  Equation25 Signal Delay (RG6 NVP Correction Coefficient)

A signal delay and the distance between two nodes (e.g., where a POE islocated in a home) may be determined as follows:

$\mspace{20mu} {{FFT}_{Scale} = {\frac{512}{4096} = {.125}}}$$\mspace{20mu} {{Index}_{{FrequencyThreshold}{({FT})}} = {\frac{20*10^{- 9}}{{.125}\left( \left\lbrack {100*10^{6}} \right\rbrack^{- 1} \right)} = 16}}$  FFT_([HMP]) = 149  Signal  Delay_(RT) = [149 * .125] * ([100 * 10⁶]⁻¹) = 186.25 * 10⁻⁹SignalDelay_((RT)Corrected) = .854030824 * 186.25 * 10⁻⁹ = 159.06 * 10⁻⁹  POE_(ReflectionPoint) = (159.06 * 10⁻⁹) * .5 = 79.5 * 10⁻⁹ ≈ 79.5  Feet

The ODFM population data may be analyzed using a standard distributionmodel. The presence and/or absence of a POE may be determined along withminimum RF impairments that may cause instability in the within the MoCAoccupied bandwidth. The OFDM blocks independently within the given MoCAchannel selected in the ACA operation.

When evaluating the ACA probe data, guard band subcarriers may beremoved to determine standard deviation and mean values. The probe datamay be normalized to represent information regarding suckouts.

An indication of a suckout may be found in a skewness result. In FIG.50A, by leaving the guard band magnitude values in the distributionmodel, the population of data may be skewed to the right. FIG. 50A showsa Raw ACA Data MoCA Channel with a Mean of −28.73, a SD of 4.29, and aSkewness of −2.62. FIG. 50B shows a Raw ACA Data Mean Crossing LeftBlock with a Mean of −26.10, a SD of 1.29, and a Skewness: 0.03. FIG.50C shows a Raw ACA Data Mean Crossing Right Block with a Mean of−29.31, a SD of 1.18, and a Skewness of −0.14. In FIG. 50B and FIG. 50Cthe skewness value indicates no suckout. The model may show data pointswith little population frequency, starting with points lower than −29dBm.

−1>x>1  Equation 26 Suckout Indicator using Skewness

As with FIG. 50B and FIG. 50C, when calculating the SD, and observingthe plots, the points may be no greater than ˜3 dB. For both plots 2σmay be less than +/−3 dB and greater than 95% of all the points. Bothfigures may indicate a relatively flat (but not zero) response for eachOFDM block.

FIG. 51 shows POE Detection Away from the Input of the Splitter with aMean of −28.91 dBm, a SD of 2.05 dB, and a Skew of −0.16. Afterperforming a 3-Point MA on a single sideband, the key metrics withoutspecial detections may be the SD and Skew. 2σ (4.1 dB) may be 95% of theobserved representation in the spread of the data.

The wider the spread, the stronger the reflection or echo of the signalmay be. A skewness of less than −1 may indicate the population is withinthe means. This may show the data does not suggest a suckout is present.This may be a Key Performance Indicator for determining the performanceof a splitter of connected devices performing the ACA.

A POE filter may be determined to exist, like as shown in FIG. 47,within 10 feet of the distance between the Node to Node connection andthe POE. Equation 27 may be used with the data collected from theRecursive Line Check Algorithm.

$\begin{matrix}{\mspace{20mu} {{{{Signal}\mspace{14mu} {Delay}} - {{POE}\mspace{14mu} {Distance}\mspace{14mu} {{Calculation}\left( {{.5}\left\lbrack {\left( \frac{{{Delta}_{Peak} \cdot {size}} + {{Delta}_{Valley} \cdot {size}}}{2} \right) \times 50\mspace{14mu} {MHz}^{- 1}} \right\rbrack} \right)}}} \approx {SignalDelay}_{POEDistance}}} & {{Equation}\mspace{14mu} 27}\end{matrix}$

A Time Domain Reflectometer (TDR) may measure reflections along aconductor. To measure those reflections, the TDR may transmit anincident signal onto the conductor and listen for its reflections. Ifthe conductor is of a uniform impedance and is properly terminated, thenthere may be no reflections, and the remaining incident signal may beabsorbed in the far-end by the termination. Instead, if there areimpedance variations, then some of the incident signals may be reflectedback to the source.

Both Line-Crossing and Fourier Transform techniques may be used todetermine the distance of the POE relative to the two devices that areperforming the EVM probe. The Line-Crossing method may have an accuracy˜10 feet, whereas the FT may have an accuracy of 2 to 8 feet. The mainvariable that may significantly swing the calculation may be the NominalVelocity of Propagation (NVP) of the cable. RG6 may have a NVP of 84%,but in some cases, it may be as low as 82%.

The MOCA20-MIB::mocaIfAcaTotalRxPower may be used to determine theoverall home coax insertion loss and its relationship to the expectedPHY rate.

A probability that a POE is connected to the input of the splitter maybe determined based at least on the relationship between the number ofnode or splitter ports, the received power, and/or insertion loss ofsplitter, as further described below.

A transmitting Node may have a maximum total output power between −3 dBmand +5 dBm at every supported MoCA channel frequency within thefrequency band of 100 MHz around the center frequency of the transmittedsignal when transmitting in MoCA 2.0 PHY.

TABLE 15 Summary of MoCA Transmit Power Number Channel Maximum Output ofChannels Bandwidth Maximum Output Power Total for all VersionTransmitted (MHz) Power per Channel Transmitted Channels 1.0/1.1 1 50 −1dBm to +7 dBm −1 dBm to +7 dBm 2.0 1 100 −3 dBm to +5 dBm  0 dBm to +8dBm 2 100 −4.5 dBm to +3.5 dBm +0.3 dBm to +8.3 dBm 2.5 3 100 −5.3 dBmto +2.7 dBm +0.7 dBm to +8.7 dBm 4 100 −6 dBm to +2 dBm +1 dBm to +9 dBm5 100 −1 dBm to +7 dBm −1 dBm to +7 dBm

The insertion loss of a 2-Way splitter@1150 MHz may be ˜7 dB.

TABLE 16 NMF Splitter Port to Insertion Loss + POE Number of PortsInsertion Loss @ 1150 MHz + POE 2 ~14 dB 4 ~28 dB 8 ~42 dB

Table 17 shows use of the ACA EVM probe data and known splittercharacteristic, which may illustrate the difference between an absenceof a POE and a connected POE to the input of a 2way splitter. Thelikelihood that a POE is or is not connected to a splitter may bedetermined based on the relationship between the number of node orsplitter ports and the received power.

TABLE 17 Two-Node—2Way Splitter Scenario Insertion Loss Matrix${{RG}\; 6\mspace{14mu} {Cable}\mspace{14mu} {Loss}} = {{\frac{6.5\mspace{14mu} {dB}}{100\mspace{14mu} {Feet}}@1150}\mspace{14mu} {MHz}}$2WAY POE + 2WAY POE + 80 + 2WAY Insertion Loss 25 dB 14 dB 24.4 dBMeasure Rx −21 dBm −11 dBm −18 dBm Power via EVM Probe Estimated −24 dB. . . −15 dB . . . −25.4 dB . . . Insertion Loss −18 dB −7 dB −17.4 dB

Table 18 shows PHY Rate over the minimum receive sensitivityrequirements. When determining the expected PHY Rate of MoCA homenetwork, the performance manager 312 may determine the total number ofdevices in the network to calculate the expected insertion loss andreceive power. A higher number of nodes in a network may impact theoverall PHY rate of the network.

TABLE 18 Minimum Receive Power Minimum Receive Version Band Power PHYRate 1.0/1.1 D −51 dBm (−2.25 dBmV) 225 Mbps E, F −49 dBm (−0.25 dBmV)240 Mbps 2.0 D −44 dBm 600 Mbps/Channel E, F −43 dBm 600 Mbps/Channel2.5 D, E, F −42 dBm 650 Mbps/Channel

If the number of nodes exceeds eight, the overall network PHY rate maybe determined by the insertion loss reference in Table 16.

FIGS. 54A-54E illustrate how the effects of a POE may be differentdepending on cable configuration. For example, the EVM probe fromNode1|Node3 may demonstrate a ˜5 dB_(Pk-Pk) ripple roughly a seconddelay signal of 60 ns. This may have a minimum impact on the overallthroughput as depicted in the bits-per-symbol Node1_(Tx)|Node3_(Rx). TheEVM probe from Node2|Node3 may demonstrate a ˜15 dB_(Pk-Pk) ripple and asecond delay signal of 180 ns. This may have an impact on the overallthroughput as depicted in the bits-per-symbol Node2_(Tx)|Node3_(Rx).

Key Performance Indicators (KPI) may be determined that determine thehealth of the MoCA network from the Physical Layer POV. Tables 19-23illustrate example KPI.

TABLE 19 Frequency Response - Suckout Detection - KPI Frequency ResponseFrequency Response + MA (Good) + MA Suckout Detection OFDM Left OFDMRight OFDM Left OFDM Right Block Block Block Block Standard  x < 0.8 dBmx > 1.5 dBm Deviation Skewness −1 < x < 1 x < −1; x > 1 Rx Channel x >{x} dBm Power

TABLE 20 Frequency Response - MoCA Friendly Splitter - KPI FrequencyFrequency Frequency Response Response Response (Good) + MA (Good) + MA(Poor) + MA MoCA Friendly Standard Splitter Splitter Tilt NA NegativeTilt Positive Tilt Standard x < 0.8 dBm x < 0.8 dBm Deviation Slope ±3dB −9 dB < x < −3 dB 3 dB > x < 9 dB Delta

TABLE 21 POE Detection - KPI POE Detection (Strong POE Detection (WeakReflection) + MA Reflection) + MA OFDM Left OFDM Right OFDM Left OFDMRight Block Block Block Block Standard x > 2.0 dBm 1.5 dBm > x < 2.0 dBmDeviation Skewness −1 < x < 1 −1 < x < 1 

TABLE 22 MoCA MeshScMod - KPI MoCA MeshScMod (Bit Load) MoCA MeshScMod(Bit Load) Excellent Minimum (Good) OFDM Left OFDM Right OFDM Left OFDMRight Block Block Block Block Mean      9 bpsym     7-8 bpsym Standard x< 1 x > 1 Deviation Skewness −1 < x < 1 −1 > x > 1 Calculated x > 848.75Mbps x > 656.25 Mbps PHY Rate Reported   x > 650 Mbps   x > 600 Mbps PHYRate

The data from Table 22 may be a result of an assumption that the XG orXB is connected to the end of the splitter network. Having the XB or XGgateways closer to the input of the splitter network may improve thereceive power by not requiring to crossover from one end of the splitternetwork to the other. This configuration may improve the receive signalpower by ˜7 dB in an 8Way splitter as shown in FIG. 55. Table 23 shows aTx-Node+8Way NMF Splitter Network where a POE is connected to the inputof the splitter.

TABLE 23 NMF 2Way Splitter Network vs. PHY Rate - KPI NMF 2Way SplitterNetwork (Max Tx @ 2.5 dBm) Insertion Loss/Rx Number Power of Nodes WithPOE* PHY Rate Insertion Loss PHY Rate 2 ~14 dB/−11.5 dBm >600 Mbps ~25dB/−22.5 dBm >600 Mbps 4 ~28 dB/−25.5 dBm >600 Mbps ~39 dB/−36.5dBm >600 Mbps 6 ~42 dB/−39.5 dBm >600 Mbps ~67 dB/−64 dBm   >200-100Mbps 8 ~42 dB/−39.5 dBm >600 Mbps 9 ~49 dB/−46.5 dBm >600 Mbps 10 ~56dB/−53 dBm   >330 Mbps 11-15 >63 dB/−60.5 dBm >200-100 Mbps

FIG. 56 shows a method 5600 for detecting and determining the distanceof a point of entry filter. Method 5600 may begin at block 5602 whereprobe data such as, for example, Alternative Channel Assessment (ACA)data may be received, from a network associated with a premises (e.g.,MoCA network). As described herein, MoCA enabled devices may monitor,analyze, and send probe data to remote computing devices for analysis.At block 5604, it may be determined whether the probe data is preparedfor processing, such as, for example, prepared for Fourier analysis. Ifit may be determined that the probe data is not prepared for processing(block 5604: NO), then control proceeds to block 5606. If it isdetermined that the probe data is prepared for processing (block 5604:YES), control may proceed to block 5610.

At block 5606, the probe data may be normalized by zero-padding samplepoints of the probe data (e.g., adding 512 zeros to the 512 samplepoints of the probe data). The probe data may be zero-padded so that theprobe data has a number of sample points that are a power of two (e.g.,1024). At block 5608, it may be determined that, based on the probe dataand based on the normalized probe data, an amount that the probe datahas been scaled. Control may proceed to block 5610.

At block 5610, an FFT response may be determined for the probe data. Theinitial sample points within a threshold region of the FFT response maynot render a ripple for which the existence or distance of a POE filtermay be determined. Accordingly, at block 5612, a threshold region of theFFT response may be determined. The threshold region may be excludedfrom the analysis of method 5600. At block 5614, t a highest peak of theFFT response outside of the threshold region identified at block 5612may be determined.

At block 5616, a ripple frequency may be determined based on the peakdetermined at block 5614. At block 5618, the ripple frequency and/or thepeak magnitude may be compared to POE filter profiles, which may bestored in the KPI database 318, for matches. If it is determined that amatching POE filter profile is identified (block 5618: YES), controlproceeds to block 5620. Otherwise (block 5618: NO), it may be determinedthat a POE filter does not exist at a premises and method 5600 may ceaseoperation. If a POE filter is present, a confirmation signal may begenerated and sent to central office and/or field technicians. If a POEfilter is not present, an alert signal may be generated and sent tocentral office and/or field technicians

At block 5622, a signal delay may be determined based on the ripplefrequency determined at block 5616 and based on the scale factordetermined at block 5608. At block 5624, the signal delay may beadjusted based on a propagation delay of the electrical connections ofthe network. At block 5626, a distance between a POE filter and anothernode of the network (e.g., a root splitter) may be determined.Thereafter, method 5600 may cease operation.

EXAMPLES Example 1—ACAPowerProfile

{    “SourceNodeID”: 0,    “AcaPowerProfileRF”: {    “AcaPowerProfileTiltDelta”: 2.542965683525839,     “AcaOfdmMean”:−29.9921875,     “RxAcaChanPwrCalc_dBm”: −0.05292069541067507,    “AcaOfdmStDev”: 5.657540687794555,     “RxChanPwrMeas_dBm”: −18,    “AcaOfdmSkewness”: −1.0483031155954898,     “AcaGraphData”: [{      “SubCarFreq”: 1.1501953125E9,       “dBmV”: 2.750612633917001,      “AcaPowerProfileTilt”: −30.776468456966377,       “dBm”: −46    }, . . . .     {       “SubCarFreq”: 1.25E9,       “dBmV”:2.750612633917001,       “AcaPowerProfileTilt”: −28.233502773440538,      “dBm”: −46     }],     “ofdmChanPwrCorrection_dBm”:−0.05292069541067507    },    “MoCAChannel”: 1150,    “AcaType”: 1,   “ReportNodeID”: 1,    “SourceNodeMacAddress”: “04:4e:5a:1a:ad:89”,   “ReportNodeMacAddress”: “04:4e:5a:1a:ac:ce” }

Example 2—MeshScMod

{    “MoCAChan”: 1150,    “SubCarModData”: [{     “PhyRateEst”:8.974609375E8,     “TxNodeID”: 0,     “RxNodeID”: 1,    “RxNodeMacAddr”: “5c:e3:0e:d9:P9:90”,     “BitLoad”: “0, 0, 0, 0, 6,8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,9, 9, 10, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 9, 10, 10, 10, 9, 9, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 9, 10, 10, 10, 9, 9, 9, 9, 9, 10, 10, 10, 10, 9, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 9, 9, 10, 10, 10, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,9, 9, 9, 9, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 9, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,9, 10, 9, 9, 10, 9, 9, 9, 9, 9, 9, 9, 10, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 9, 9, 10, 10, 10, 10, 10, 9, 10, 9, 9, 9, 9, 9, 10,9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10,9, 10, 9, 10, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 9, 10, 9, 9, 9, 9, 9, 8, 8,8, 7, 0, 0, 0”,     “TxNodeMacAddr”: “04:4e:5a:1a:ac:ce”    },    {    “PhyRateEst”: 8.984375E8,     “TxNodeID”: 1,     “RxNodeID”: 0,    “RxNodeMacAddr”: “04:4e:5a:1a:ac:ce”,     “BitLoad”: “0, 0, 0, 0, 6,6, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,8, 8, 8, 8, 8, 8, 9, 8, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 9, 10, 9, 9, 9, 10,9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 9, 9, 10,9, 10, 10, 10, 10, 10, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 9, 10, 9, 10, 10,10, 10, 9, 9, 10, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 9, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 9, 10, 9, 10, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,9, 9, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 9, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 9, 10, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,10, 9, 10, 10, 9, 9, 9, 10, 9, 9, 9, 10, 9, 10, 10, 9, 9, 10, 10, 9, 9,9, 9, 10, 9, 10, 10, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 7, 7, 6, 0, 0, 0”,    “TxNodeMacAddr”: “5c:e3:0e:d9:f9:90”    }],    “ScModType”:“UNICAST” }

Example 3—Recursive Line Crossing Analysis (Right OFDM Block)

{    “NumberOfTiltCrossings”: 16,    “EvmProbeData”: “−25.5, −25.2,−25.5, −25.9, −26.4, −27.0, −27.7, −28.7, −29.8, −30.8, −31.8, −32.6,−33.0, −33.2, −33.2, −33.0, −32.5, −31.8, −31.0, −30.0, −28.9, −28.0,−27.3, −27.0, −26.5, −26.3, −26.1, −26.0, −25.9, −26.1, −26.6, −27.2,−28.0, −28.7, −29.9, −31.1, −31.8, −32.6, −33.2, −33.4, −33.4, −33.0,−32.5, −31.7, −30.6, −29.3, −28.4, −27.5, −26.7, −26.2, −25.8, −25.5,−25.4, −25.2, −25.3, −25.7, −26.4, −27.1, −27.9, −28.9, −30.0, −31.1,−31.9, −32.5, −33.0, −33.2, −32.9, −32.5, −31.7, −30.8, −29.8, −29.0,−28.0, −27.5, −26.9, −26.6, −26.5, −26.3, −26.6, −26.8, −26.9, −27.1,−27.8, −28.4, −29.1, −29.8, −30.9, −31.9, −32.5, −32.9, −33.2, −33.2,−32.9, −32.4, −31.6, −30.6, −29.3, −28.2, −27.4, −26.7, −26.0, −25.6,−25.2, −25.2, −25.4, −25.6, −25.9, −26.3, −26.9, −27.8, −28.8, −29.9,−31.0, −31.8, −32.4, −33.0, −33.1, −32.9, −32.4, −31.6, −30.7, −29.6,−28.5, −27.5, −26.6, −25.7, −25.3, −25.2, −24.9, −24.9, −24.9, −25.0,−25.2, −25.7, −26.4, −27.3, −28.3, −29.2, −30.2, −30.9, −31.4, −31.6,−31.7, −31.2, −30.8, −29.9, −28.8, −27.7, −26.6, −25.5, −24.8, −24.5,−24.1, −24.0, −23.8, −23.9, −24.3, −24.6, −25.2, −26.1, −26.9, −27.8,−28.9, −30.0, −30.8, −31.4, −31.5, −31.7, −31.5, −30.9, −30.2, −29.2,−28.0, −27.1, −26.3, −25.6, −25.0, −24.4, −24.1, −24.1, −24.1, −24.1,−24.3, −24.3, −24.6, −25.2, −26.1, −27.2, −28.3, −29.3, −30.1, −30.6,−30.9, −31.1, −30.9, −30.4, −29.6, −28.5, −27.4, −26.2, −25.5, −24.8,−24.4, −23.9, −23.7, −23.6, −23.6, −23.9, −23.88888888888889, −24.0,−23.857142857142858, −24.166666666666668, −24.2, −24.5,−24.666666666666668, −25.0, −26.0”,    “OfdmSideBand”: “RIGHT_BLOCK”,   “MocaChannel”: “1150MHz”,    “DescriptiveStatisticsUpper”: [{    “Mean”: 12.76923076923077,     “SD”: 0.8320502943378438,     “Min”:11,     “Max”: 14,     “Skewness”: −0.5280857928709914,    “SlopeDelta”: “”    }],   “EvmProbeTiltLine”:  “−29.95438626866355,  −29.9379831941107,  −29.92158011955785,−29.905177045004997, −29.888773970452146, −29.872370895899294,−29.855967821346443, −29.83956474679359, −29.82316167224074,−29.80675859768789, −29.790355523135037, −29.773952448582186,−29.757549374029338, −29.741146299476487, −29.724743224923635,−29.708340150370784, −29.691937075817933, −29.67553400126508,−29.65913092671223, −29.64272785215938, −29.626324777606527,−29.609921703053676, −29.593518628500824, −29.577115553947973,−29.56071247939512, −29.54430940484227, −29.52790633028942,−29.511503255736567, −29.495100181183716, −29.478697106630865,−29.462294032078013, −29.44589095752516, −29.42948788297231,−29.41308480841946, −29.39668173386661, −29.38027865931376,−29.36387558476091, −29.347472510208057, −29.331069435655206,−29.314666361102354, −29.298263286549503, −29.28186021199665,−29.2654571374438, −29.24905406289095, −29.232650988338097,−29.216247913785246, −29.199844839232394, −29.183441764679543,−29.16703869012669, −29.15063561557384, −29.13423254102099,−29.117829466468137, −29.101426391915286, −29.085023317362435,−29.068620242809583, −29.052217168256732, −29.035814093703884,−29.019411019151033, −29.00300794459818, −28.98660487004533,−28.97020179549248, −28.953798720939627, −28.937395646386776,−28.920992571833924, −28.904589497281073, −28.88818642272822,−28.87178334817537, −28.85538027362252, −28.838977199069667,−28.822574124516816, −28.806171049963964, −28.789767975411113,−28.77336490085826, −28.75696182630541, −28.74055875175256,−28.724155677199708, −28.707752602646856, −28.691349528094005,−28.674946453541153, −28.658543378988306, −28.642140304435454,−28.625737229882603, −28.60933415532975, −28.5929310807769,−28.57652800622405, −28.560124931671197, −28.543721857118346,−28.527318782565494, −28.510915708012643, −28.49451263345979,−28.47810955890694, −28.46170648435409, −28.445303409801237,−28.428900335248386, −28.412497260695535, −28.396094186142683,−28.379691111589832, −28.36328803703698, −28.34688496248413,−28.330481887931278, −28.31407881337843, −28.29767573882558,−28.281272664272727, −28.264869589719876, −28.248466515167024,−28.232063440614173, −28.21566036606132, −28.19925729150847,−28.18285421695562, −28.166451142402767, −28.150048067849916,−28.133644993297064, −28.117241918744213, −28.10083884419136,−28.08443576963851, −28.06803269508566, −28.051629620532808,−28.035226545979956, −28.018823471427105, −28.002420396874253,−27.986017322321402, −27.96961424776855, −27.9532111732157,−27.93680809866285, −27.92040502411, −27.90400194955715,−27.887598875004297, −27.871195800451446, −27.854792725898594,−27.838389651345743, −27.82198657679289, −27.80558350224004,−27.78918042768719, −27.772777353134337, −27.756374278581486,−27.739971204028635, −27.723568129475783, −27.707165054922932,−27.69076198037008, −27.67435890581723, −27.657955831264378,−27.641552756711526, −27.625149682158675, −27.608746607605823,−27.592343533052976, −27.57594045850012, −27.559537383947273,−27.54313430939442, −27.52673123484157, −27.51032816028872,−27.493925085735867, −27.477522011183016, −27.461118936630164,−27.444715862077313, −27.42831278752446, −27.41190971297161,−27.39550663841876, −27.379103563865907, −27.362700489313056,−27.346297414760205, −27.329894340207353, −27.313491265654502,−27.29708819110165, −27.2806851165488, −27.264282041995948,−27.247878967443096, −27.231475892890245, −27.215072818337397,−27.198669743784542, −27.182266669231694, −27.165863594678843,−27.14946052012599, −27.13305744557314, −27.11665437102029,−27.100251296467437, −27.083848221914586, −27.067445147361735,−27.051042072808883, −27.034638998256032, −27.01823592370318,−27.00183284915033, −26.985429774597478, −26.969026700044626,−26.952623625491775, −26.936220550938923, −26.919817476386072,−26.90341440183322, −26.88701132728037, −26.87060825272752,−26.854205178174666, −26.83780210362182, −26.821399029068967,−26.804995954516116, −26.788592879963264, −26.772189805410413,−26.75578673085756, −26.73938365630471, −26.72298058175186,−26.706577507199007, −26.690174432646156, −26.673771358093305,−26.657368283540453, −26.640965208987602, −26.62456213443475,−26.6081590598819, −26.591755985329048, −26.575352910776196,−26.558949836223345, −26.542546761670494, −26.526143687117642,−26.50974061256479, −26.493337538011943, −26.476934463459088,−26.46053138890624, −26.44412831435339, −26.427725239800537,−26.411322165247686”,    “DescriptiveStatisticsLower”: [{     “Mean”:12.6,     “SD”: 1.7647338933351155,     “Min”: 10,     “Max”: 15,    “Skewness”: 0.16915751258199244,     “SlopeDelta”: “”    }] }

Example 4—POE Detection (POE+75'+3WAY MFS)

{   “SampleRate”: 1.0E8,   “RoundTripSignalTimeCorrected”:1.5950507409700002E−7,   “ FFTHighestMagnitudePeakIndex ”: 149,  “20nsFFTIndexThreshold”: 16,   “FFTScale”: 0.125,  “RoundTripDelaySignalTime”: 1.8625000000000002E−7,  “RoundTripDelaySignalFeet”: 159.505074097 }

The above discussion is by way of example, and modifications may be madeas desired for different implementations. For example, steps and/orcomponents may be subdivided, combined, rearranged, removed,supplemented, and/or augmented; performed on a single device or aplurality of devices; performed in parallel, in series; or anycombination thereof. Additional features may be added.

We claim:
 1. A method comprising: receiving, by a computing device andfrom a first device associated with a premises, probe data indicative ofsignal loss between the first device and a second device; determining,based on the probe data for a plurality of frequencies, a frequencyresponse; determining, based on comparing a peak of the frequencyresponse with one or more profiles, that the first device or the seconddevice comprises a Point of Entry (POE) filter; determining, based on asignal delay associated with the peak, a distance between the firstdevice and the second device; and sending, based on determining that thefirst device or the second device comprises the POE filter and based ondetermining that the distance between the first device and the seconddevice satisfies a threshold, an alert signal.
 2. The method of claim 1,wherein the one or more profiles comprises POE filter profiles.
 3. Themethod of claim 1, further comprising: determining a threshold region ofthe frequency response; and determining a highest magnitude peak of thefrequency response outside of the threshold region.
 4. The method ofclaim 1, further comprising: determining, based on a connection typebetween the first device and the second device, a propagation delay; andadjusting, based on the propagation delay, the signal delay.
 5. Themethod of claim 1, further comprising: normalizing the probe data byzero-padding sample points of the probe data; and determining, based onthe probe data and based on the normalized probe data, a frequencyscale; wherein determining the signal delay is further based on thefrequency scale.
 6. The method of claim 1, wherein determining that thedistance between the first device and the second device satisfies thethreshold comprises determining that the distance between the firstdevice and the second device is greater than ten feet.
 7. A methodcomprising: receiving, by a computing device remote and from a premisesfrom a first device at the premises, probe data indicative of signalloss between the first device and a second device at the premises;determining, based on the probe data for a plurality of frequencies, afrequency response; comparing a peak of the frequency response with oneor more Point of Entry (POE) filter profiles; determining, based on thecomparing, whether the first device or the second device comprises a POEfilter; and generating, based on determining that the first device orthe second device comprises the POE filter, a confirmation signal. 8.The method of claim 7, further comprising: sending, by the computingdevice, via a simple network management protocol, and to the clientdevice, instructions to measure characteristics of a network comprisingthe first device and the second device.
 9. The method of claim 7,further comprising: determining a threshold region of the frequencyresponse; and wherein comparing the peak of the frequency response withthe one or more POE filter profiles further comprises selecting ahighest magnitude peak of the frequency response outside of thethreshold region as the peak of the frequency response.
 10. The methodof claim 7, further comprising: determining whether a continuous wavepath loss between the first device and the second device is between −40decibel millivolts (dBmV) and 40 dBmV; wherein determining whether thefirst device or the second device comprises the POE filter is furtherbased on determining that the continuous wave path loss between the twonodes is greater than 40 dBmV or lower than −40 dBmV.
 11. The method ofclaim 7, further comprising: normalizing the probe data by zero-paddingsample points of the probe data; determining, based on the probe dataand based on the normalized probe data, a frequency scale; anddetermining, based on the frequency scale and based on the peak of thefrequency response, a signal delay.
 12. The method of claim 7, furthercomprising: determining, based on the peak, a signal delay; adjusting,based on a propagation delay associated with the network, the signaldelay; and determining, based on the adjusted signal delay, a distancebetween the first device and the second device.
 13. The method of claim12, further comprising: sending, based on determining that the distancebetween the first device and the second device is within ten feet, aconfirmation signal.
 14. A method comprising: receiving, by a computingdevice and from a first device associated with a premises, probe dataindicative of signal loss between the first device and a second device;determining, based on the probe data, a frequency response; determininga region of the frequency response corresponding to signal delays ofless than a threshold amount of time; determining a peak of thefrequency response outside of the determined region; determining, basedon the peak and based on a propagation delay associated with thenetwork, a signal delay; determining, based on the signal delay, adistance between the first device and the second device; and sending,based on determining that the distance satisfies a threshold distance,an alert signal.
 15. The method of claim 14, further comprising:determining, based on comparing the peak with one or more Point of Entry(POE) filter profiles, that the first device or the second devicecomprises a POE filter.
 16. The method of claim 14, wherein thecomputing device is remote from the premises.
 17. The method of claim14, further comprising determining that the first device or the seconddevice comprises a root splitter.
 18. The method of claim 14, furthercomprising: normalizing the probe data by zero-padding sample points ofthe probe data; and determining, based on the probe data and based onthe normalized probe data, a frequency scale; wherein determining thesignal delay is further based on the frequency scale.
 19. The method ofclaim 14, wherein determining that the distance satisfies the thresholddistance comprises determining that the distance between the firstdevice and the second device is greater than ten feet.
 20. The method ofclaim 14, wherein the threshold amount of time comprises twentynanoseconds.